diff --git a/skore/src/skore/_sklearn/_plot/base.py b/skore/src/skore/_sklearn/_plot/base.py
index 821e1311a1..44cef6856c 100644
--- a/skore/src/skore/_sklearn/_plot/base.py
+++ b/skore/src/skore/_sklearn/_plot/base.py
@@ -1,11 +1,30 @@
+import inspect
+from collections.abc import Callable
+from functools import wraps
+from io import StringIO
 from typing import Any, Protocol, runtime_checkable
 
+import matplotlib.pyplot as plt
 import pandas as pd
+from rich.console import Console
+from rich.panel import Panel
+from rich.tree import Tree
+
+from skore._config import get_config
+from skore._sklearn.types import PlotBackend
+
+########################################################################################
+# Display protocol
+########################################################################################
 
 
 @runtime_checkable
 class Display(Protocol):
-    """Protocol specifying the common API for all `skore` displays."""
+    """Protocol specifying the common API for all `skore` displays.
+
+    .. note::
+       This class is a Python protocol and it is not intended to be inherited from.
+    """
 
     def plot(self, **kwargs: Any) -> None:
         """Display a figure containing the information of the display."""
@@ -21,3 +40,248 @@ def frame(self, **kwargs: Any) -> pd.DataFrame:
         DataFrame
             A DataFrame containing the data used to create the display.
         """
+
+    def help(self) -> None:
+        """Display available attributes and methods using rich."""
+
+
+########################################################################################
+# Plotting related mixins
+########################################################################################
+
+
+class PlotBackendMixin:
+    """Mixin class for Displays to dispatch plotting to the configured backend."""
+
+    def _plot(self, **kwargs):
+        """Dispatch plotting to the configured backend."""
+        plot_backend = get_config()["plot_backend"]
+        if plot_backend == "matplotlib":
+            return self._plot_matplotlib(**kwargs)
+        elif plot_backend == "plotly":
+            return self._plot_plotly(**kwargs)
+        else:
+            raise NotImplementedError(
+                f"Plotting backend {plot_backend} not available. "
+                f"Available options are {PlotBackend.__args__}."
+            )
+
+    def _plot_plotly(self, **kwargs):
+        raise NotImplementedError(
+            "Plotting with plotly is not supported for this Display."
+        )
+
+
+DEFAULT_STYLE = {
+    "font.size": 14,
+    "axes.labelsize": 14,
+    "axes.titlesize": 14,
+    "xtick.labelsize": 13,
+    "ytick.labelsize": 13,
+    "legend.fontsize": 10,
+    "legend.title_fontsize": 11,
+    "axes.linewidth": 1.25,
+    "grid.linewidth": 1.25,
+    "lines.linewidth": 1.75,
+    "lines.markersize": 6,
+    "patch.linewidth": 1.25,
+    "xtick.major.width": 1.5,
+    "ytick.major.width": 1.5,
+    "xtick.minor.width": 1.25,
+    "ytick.minor.width": 1.25,
+    "xtick.major.size": 7,
+    "ytick.major.size": 7,
+    "xtick.minor.size": 5,
+    "ytick.minor.size": 5,
+    "legend.loc": "upper left",
+    "legend.borderaxespad": 0,
+}
+
+
+class StyleDisplayMixin:
+    """Mixin to control the style plot of a display."""
+
+    @property
+    def _style_params(self) -> list[str]:
+        """Get the list of available style parameters.
+
+        Returns
+        -------
+        list
+            List of style parameter names (without '_default_' prefix).
+        """
+        prefix = "_default_"
+        suffix = "_kwargs"
+        return [
+            attr[len(prefix) :]
+            for attr in dir(self)
+            if attr.startswith(prefix) and attr.endswith(suffix)
+        ]
+
+    def set_style(self, **kwargs: Any):
+        """Set the style parameters for the display.
+
+        Parameters
+        ----------
+        **kwargs : dict
+            Style parameters to set. Each parameter name should correspond to a
+            a style attribute passed to the plot method of the display.
+
+        Returns
+        -------
+        self : object
+            Returns the instance itself.
+
+        Raises
+        ------
+        ValueError
+            If a style parameter is unknown.
+        """
+        for param_name, param_value in kwargs.items():
+            default_attr = f"_default_{param_name}"
+            if not hasattr(self, default_attr):
+                raise ValueError(
+                    f"Unknown style parameter: {param_name}. "
+                    f"The parameter name should be one of {self._style_params}."
+                )
+            setattr(self, default_attr, param_value)
+        return self
+
+    @staticmethod
+    def style_plot(plot_func: Callable) -> Callable:
+        """Apply consistent style to skore displays.
+
+        This decorator:
+        1. Applies default style settings
+        2. Executes `plot_func`
+        3. Applies `tight_layout`
+
+        Parameters
+        ----------
+        plot_func : callable
+            The plot function to be decorated.
+
+        Returns
+        -------
+        callable
+            The decorated plot function.
+        """
+
+        @wraps(plot_func)
+        def wrapper(self, *args: Any, **kwargs: Any) -> Any:
+            # We need to manually handle setting the style of the parameters because
+            # `plt.style.context` has a side effect with the interactive mode.
+            # See https://github.com/matplotlib/matplotlib/issues/25041
+            original_params = {key: plt.rcParams[key] for key in DEFAULT_STYLE}
+            plt.rcParams.update(DEFAULT_STYLE)
+            try:
+                result = plot_func(self, *args, **kwargs)
+            finally:
+                plt.rcParams.update(original_params)
+            return result
+
+        return wrapper
+
+
+########################################################################################
+# General purpose mixins
+########################################################################################
+
+
+class HelpDisplayMixin:
+    """Mixin class to add help functionality to a class."""
+
+    estimator_name: str  # defined in the concrete display class
+
+    def _get_attributes_for_help(self) -> list[str]:
+        """Get the attributes ending with '_' to display in help."""
+        attributes = []
+        for name in dir(self):
+            if name.endswith("_") and not name.startswith("_"):
+                attributes.append(f".{name}")
+        return sorted(attributes)
+
+    def _get_methods_for_help(self) -> list[tuple[str, Any]]:
+        """Get the public methods to display in help."""
+        methods = inspect.getmembers(self, predicate=inspect.ismethod)
+        filtered_methods = []
+        for name, method in methods:
+            is_private = name.startswith("_")
+            is_class_method = inspect.ismethod(method) and method.__self__ is type(self)
+            is_help_method = name == "help"
+            if not (is_private or is_class_method or is_help_method):
+                filtered_methods.append((f".{name}(...)", method))
+        return sorted(filtered_methods)
+
+    def _create_help_tree(self) -> Tree:
+        """Create a rich Tree with attributes and methods."""
+        tree = Tree("display")
+
+        attributes = self._get_attributes_for_help()
+        attr_branch = tree.add("[bold cyan] Attributes[/bold cyan]")
+        # Ensure figure_ and ax_ are first
+        sorted_attrs = sorted(attributes)
+        if ("figure_" in sorted_attrs) and ("ax_" in sorted_attrs):
+            sorted_attrs.remove(".ax_")
+            sorted_attrs.remove(".figure_")
+            sorted_attrs = [".figure_", ".ax_"] + [
+                attr for attr in sorted_attrs if attr not in [".figure_", ".ax_"]
+            ]
+        for attr in sorted_attrs:
+            attr_branch.add(attr)
+
+        methods = self._get_methods_for_help()
+        method_branch = tree.add("[bold cyan]Methods[/bold cyan]")
+        for name, method in methods:
+            description = (
+                method.__doc__.split("\n")[0]
+                if method.__doc__
+                else "No description available"
+            )
+            method_branch.add(f"{name} - {description}")
+
+        return tree
+
+    def _create_help_panel(self) -> Panel:
+        return Panel(
+            self._create_help_tree(),
+            title=f"[bold cyan]{self.__class__.__name__} [/bold cyan]",
+            border_style="orange1",
+            expand=False,
+        )
+
+    def help(self) -> None:
+        """Display available attributes and methods using rich."""
+        from skore import console  # avoid circular import
+
+        console.print(self._create_help_panel())
+
+    def __str__(self) -> str:
+        """Return a string representation using rich."""
+        string_buffer = StringIO()
+        console = Console(file=string_buffer, force_terminal=False)
+        console.print(
+            Panel(
+                "Get guidance using the help() method",
+                title=f"[cyan]{self.__class__.__name__}[/cyan]",
+                border_style="orange1",
+                expand=False,
+            )
+        )
+        return string_buffer.getvalue()
+
+    def __repr__(self) -> str:
+        """Return a string representation using rich."""
+        string_buffer = StringIO()
+        console = Console(file=string_buffer, force_terminal=False)
+        console.print(f"[cyan]skore.{self.__class__.__name__}(...)[/cyan]")
+        return string_buffer.getvalue()
+
+
+########################################################################################
+# Display mixin inheriting from the different mixins
+########################################################################################
+
+
+class DisplayMixin(HelpDisplayMixin, PlotBackendMixin, StyleDisplayMixin):
+    """Mixin inheriting help, plotting, and style functionality."""
diff --git a/skore/src/skore/_sklearn/_plot/data/table_report.py b/skore/src/skore/_sklearn/_plot/data/table_report.py
index e062675a8a..7db9b41299 100644
--- a/skore/src/skore/_sklearn/_plot/data/table_report.py
+++ b/skore/src/skore/_sklearn/_plot/data/table_report.py
@@ -16,10 +16,8 @@
 )
 
 from skore._externals._skrub_compat import sbd
-from skore._sklearn._plot.style import StyleDisplayMixin
+from skore._sklearn._plot.base import DisplayMixin
 from skore._sklearn._plot.utils import (
-    HelpDisplayMixin,
-    PlotBackendMixin,
     _adjust_fig_size,
     _rotate_ticklabels,
     _validate_style_kwargs,
@@ -162,9 +160,7 @@ def _resize_categorical_axis(
     _adjust_fig_size(figure, ax, target_width, target_height)
 
 
-class TableReportDisplay(
-    StyleDisplayMixin, HelpDisplayMixin, ReprHTMLMixin, PlotBackendMixin
-):
+class TableReportDisplay(ReprHTMLMixin, DisplayMixin):
     """Display reporting information about a given dataset.
 
     This display summarizes the dataset and provides a way to visualize
@@ -222,8 +218,8 @@ def _compute_data_for_display(cls, dataset: pd.DataFrame) -> "TableReportDisplay
         """
         return cls(summarize_dataframe(dataset, with_plots=True, title=None, verbose=0))
 
-    @StyleDisplayMixin.style_plot
-    def _plot_matplotlib(
+    @DisplayMixin.style_plot
+    def plot(
         self,
         *,
         x: str | None = None,
@@ -305,6 +301,34 @@ def _plot_matplotlib(
         >>> display = report.data.analyze()
         >>> display.plot(kind="corr")
         """
+        return self._plot(
+            x=x,
+            y=y,
+            hue=hue,
+            kind=kind,
+            top_k_categories=top_k_categories,
+            scatterplot_kwargs=scatterplot_kwargs,
+            stripplot_kwargs=stripplot_kwargs,
+            boxplot_kwargs=boxplot_kwargs,
+            heatmap_kwargs=heatmap_kwargs,
+            histplot_kwargs=histplot_kwargs,
+        )
+
+    def _plot_matplotlib(
+        self,
+        *,
+        x: str | None = None,
+        y: str | None = None,
+        hue: str | None = None,
+        kind: Literal["dist", "corr"] = "dist",
+        top_k_categories: int = 20,
+        scatterplot_kwargs: dict[str, Any] | None = None,
+        stripplot_kwargs: dict[str, Any] | None = None,
+        boxplot_kwargs: dict[str, Any] | None = None,
+        heatmap_kwargs: dict[str, Any] | None = None,
+        histplot_kwargs: dict[str, Any] | None = None,
+    ) -> None:
+        """Matplotlib implementation of the `plot` method."""
         self.figure_, self.ax_ = plt.subplots()
         if kind == "dist":
             match (x is None, y is None, hue is None):
diff --git a/skore/src/skore/_sklearn/_plot/metrics/confusion_matrix.py b/skore/src/skore/_sklearn/_plot/metrics/confusion_matrix.py
index 64dfba8df4..973d97a789 100644
--- a/skore/src/skore/_sklearn/_plot/metrics/confusion_matrix.py
+++ b/skore/src/skore/_sklearn/_plot/metrics/confusion_matrix.py
@@ -2,12 +2,10 @@
 import numpy as np
 from sklearn.metrics import confusion_matrix as sklearn_confusion_matrix
 
-from skore._sklearn._plot.base import Display
-from skore._sklearn._plot.style import StyleDisplayMixin
-from skore._sklearn._plot.utils import PlotBackendMixin
+from skore._sklearn._plot.base import DisplayMixin
 
 
-class ConfusionMatrixDisplay(PlotBackendMixin, Display):
+class ConfusionMatrixDisplay(DisplayMixin):
     """Display for confusion matrix.
 
     Parameters
@@ -45,7 +43,6 @@ class ConfusionMatrixDisplay(PlotBackendMixin, Display):
         confusion matrix.
     """
 
-    @StyleDisplayMixin.style_plot
     def __init__(
         self,
         confusion_matrix,
@@ -64,7 +61,8 @@ def __init__(
         self.ax_ = None
         self.text_ = None
 
-    def _plot_matplotlib(self, ax=None, *, cmap="Blues", colorbar=True, **kwargs):
+    @DisplayMixin.style_plot
+    def plot(self, ax=None, *, cmap="Blues", colorbar=True, **kwargs):
         """Plot the confusion matrix.
 
         Parameters
@@ -87,6 +85,9 @@ def _plot_matplotlib(self, ax=None, *, cmap="Blues", colorbar=True, **kwargs):
         self : ConfusionMatrixDisplay
             Configured with the confusion matrix.
         """
+        return self._plot(ax=ax, cmap=cmap, colorbar=colorbar, **kwargs)
+
+    def _plot_matplotlib(self, ax=None, *, cmap="Blues", colorbar=True, **kwargs):
         if self.normalize not in (None, "true", "pred", "all"):
             raise ValueError(
                 "normalize must be one of None, 'true', 'pred', 'all'; "
diff --git a/skore/src/skore/_sklearn/_plot/metrics/feature_importance_display.py b/skore/src/skore/_sklearn/_plot/metrics/feature_importance_display.py
index 10a145e474..9b9d5cb0fe 100644
--- a/skore/src/skore/_sklearn/_plot/metrics/feature_importance_display.py
+++ b/skore/src/skore/_sklearn/_plot/metrics/feature_importance_display.py
@@ -1,13 +1,9 @@
 import matplotlib.pyplot as plt
 
-from skore._sklearn._plot.base import Display
-from skore._sklearn._plot.style import StyleDisplayMixin
-from skore._sklearn._plot.utils import HelpDisplayMixin, PlotBackendMixin
+from skore._sklearn._plot.base import DisplayMixin
 
 
-class FeatureImportanceDisplay(
-    HelpDisplayMixin, StyleDisplayMixin, PlotBackendMixin, Display
-):
+class FeatureImportanceDisplay(DisplayMixin):
     """Feature importance display.
 
     Each report type produces its own output frame and plot.
@@ -102,7 +98,17 @@ def _frame_comparison_report(self):
 
         return pd.concat(self._coefficient_data, axis=1)
 
-    @StyleDisplayMixin.style_plot
+    @DisplayMixin.style_plot
+    def plot(self, **kwargs) -> None:
+        """Plot the coefficients of linear models.
+
+        Parameters
+        ----------
+        **kwargs : dict
+            Additional keyword arguments to be passed to the plot method.
+        """
+        return self._plot(**kwargs)
+
     def _plot_matplotlib(self, **kwargs):
         from skore._sklearn._comparison import ComparisonReport
         from skore._sklearn._cross_validation import CrossValidationReport
diff --git a/skore/src/skore/_sklearn/_plot/metrics/metrics_summary_display.py b/skore/src/skore/_sklearn/_plot/metrics/metrics_summary_display.py
index a6846294ff..9220960f0d 100644
--- a/skore/src/skore/_sklearn/_plot/metrics/metrics_summary_display.py
+++ b/skore/src/skore/_sklearn/_plot/metrics/metrics_summary_display.py
@@ -1,8 +1,7 @@
-from skore._sklearn._plot.style import StyleDisplayMixin
-from skore._sklearn._plot.utils import HelpDisplayMixin
+from skore._sklearn._plot.base import DisplayMixin
 
 
-class MetricsSummaryDisplay(HelpDisplayMixin, StyleDisplayMixin):
+class MetricsSummaryDisplay(DisplayMixin):
     """Display for summarize.
 
     An instance of this class will be created by `Report.metrics.summarize()`.
@@ -22,6 +21,7 @@ def frame(self):
         """
         return self.summarize_data
 
-    @StyleDisplayMixin.style_plot
+    @DisplayMixin.style_plot
     def plot(self):
+        """Not yet implemented."""
         raise NotImplementedError
diff --git a/skore/src/skore/_sklearn/_plot/metrics/precision_recall_curve.py b/skore/src/skore/_sklearn/_plot/metrics/precision_recall_curve.py
index 2115d6c3fc..b2aba04adb 100644
--- a/skore/src/skore/_sklearn/_plot/metrics/precision_recall_curve.py
+++ b/skore/src/skore/_sklearn/_plot/metrics/precision_recall_curve.py
@@ -12,11 +12,9 @@
 from sklearn.metrics import average_precision_score, precision_recall_curve
 from sklearn.preprocessing import LabelBinarizer
 
-from skore._sklearn._plot.style import StyleDisplayMixin
+from skore._sklearn._plot.base import DisplayMixin
 from skore._sklearn._plot.utils import (
     LINESTYLE,
-    HelpDisplayMixin,
-    PlotBackendMixin,
     _ClassifierCurveDisplayMixin,
     _despine_matplotlib_axis,
     _validate_style_kwargs,
@@ -45,9 +43,7 @@ def _set_axis_labels(ax: Axes, info_pos_label: str | None) -> None:
 MAX_N_LABELS = 5
 
 
-class PrecisionRecallCurveDisplay(
-    StyleDisplayMixin, HelpDisplayMixin, _ClassifierCurveDisplayMixin, PlotBackendMixin
-):
+class PrecisionRecallCurveDisplay(_ClassifierCurveDisplayMixin, DisplayMixin):
     """Precision Recall visualization.
 
     An instance of this class should be created by
@@ -630,8 +626,8 @@ def _plot_comparison_cross_validation(
 
         return self.ax_, lines, info_pos_label
 
-    @StyleDisplayMixin.style_plot
-    def _plot_matplotlib(
+    @DisplayMixin.style_plot
+    def plot(
         self,
         *,
         estimator_name: str | None = None,
@@ -640,8 +636,6 @@ def _plot_matplotlib(
     ) -> None:
         """Plot visualization.
 
-        Extra keyword arguments will be passed to matplotlib's `plot`.
-
         Parameters
         ----------
         estimator_name : str, default=None
@@ -679,6 +673,20 @@ def _plot_matplotlib(
         >>> display = report.metrics.precision_recall()
         >>> display.plot(pr_curve_kwargs={"color": "tab:red"})
         """
+        return self._plot(
+            estimator_name=estimator_name,
+            pr_curve_kwargs=pr_curve_kwargs,
+            despine=despine,
+        )
+
+    def _plot_matplotlib(
+        self,
+        *,
+        estimator_name: str | None = None,
+        pr_curve_kwargs: dict[str, Any] | list[dict[str, Any]] | None = None,
+        despine: bool = True,
+    ) -> None:
+        """Matplotlib implementation of the `plot` method."""
         if (
             self.report_type == "comparison-cross-validation"
             and self.ml_task == "multiclass-classification"
diff --git a/skore/src/skore/_sklearn/_plot/metrics/prediction_error.py b/skore/src/skore/_sklearn/_plot/metrics/prediction_error.py
index d489c16062..546e09cc8e 100644
--- a/skore/src/skore/_sklearn/_plot/metrics/prediction_error.py
+++ b/skore/src/skore/_sklearn/_plot/metrics/prediction_error.py
@@ -10,10 +10,8 @@
 from sklearn.utils.validation import _num_samples, check_array
 
 from skore._externals._sklearn_compat import _safe_indexing
-from skore._sklearn._plot.style import StyleDisplayMixin
+from skore._sklearn._plot.base import DisplayMixin
 from skore._sklearn._plot.utils import (
-    HelpDisplayMixin,
-    PlotBackendMixin,
     _despine_matplotlib_axis,
     _validate_style_kwargs,
     sample_mpl_colormap,
@@ -25,7 +23,7 @@
 MAX_N_LABELS = 6  # 5 + 1 for the perfect model line
 
 
-class PredictionErrorDisplay(StyleDisplayMixin, HelpDisplayMixin, PlotBackendMixin):
+class PredictionErrorDisplay(DisplayMixin):
     """Visualization of the prediction error of a regression model.
 
     This tool can display "residuals vs predicted" or "actual vs predicted"
@@ -522,8 +520,8 @@ def _plot_comparison_cross_validation(
 
         return scatter
 
-    @StyleDisplayMixin.style_plot
-    def _plot_matplotlib(
+    @DisplayMixin.style_plot
+    def plot(
         self,
         *,
         estimator_name: str | None = None,
@@ -578,6 +576,26 @@ def _plot_matplotlib(
         >>> display = report.metrics.prediction_error()
         >>> display.plot(kind="actual_vs_predicted")
         """
+        return self._plot(
+            estimator_name=estimator_name,
+            kind=kind,
+            data_points_kwargs=data_points_kwargs,
+            perfect_model_kwargs=perfect_model_kwargs,
+            despine=despine,
+        )
+
+    def _plot_matplotlib(
+        self,
+        *,
+        estimator_name: str | None = None,
+        kind: Literal[
+            "actual_vs_predicted", "residual_vs_predicted"
+        ] = "residual_vs_predicted",
+        data_points_kwargs: dict[str, Any] | list[dict[str, Any]] | None = None,
+        perfect_model_kwargs: dict[str, Any] | None = None,
+        despine: bool = True,
+    ) -> None:
+        """Matplolib implementation of the `plot` method."""
         expected_kind = ("actual_vs_predicted", "residual_vs_predicted")
         if kind not in expected_kind:
             raise ValueError(
diff --git a/skore/src/skore/_sklearn/_plot/metrics/roc_curve.py b/skore/src/skore/_sklearn/_plot/metrics/roc_curve.py
index 33a8288a3a..96dc0031ef 100644
--- a/skore/src/skore/_sklearn/_plot/metrics/roc_curve.py
+++ b/skore/src/skore/_sklearn/_plot/metrics/roc_curve.py
@@ -11,11 +11,9 @@
 from sklearn.metrics import auc, roc_curve
 from sklearn.preprocessing import LabelBinarizer
 
-from skore._sklearn._plot.style import StyleDisplayMixin
+from skore._sklearn._plot.base import DisplayMixin
 from skore._sklearn._plot.utils import (
     LINESTYLE,
-    HelpDisplayMixin,
-    PlotBackendMixin,
     _ClassifierCurveDisplayMixin,
     _despine_matplotlib_axis,
     _validate_style_kwargs,
@@ -63,9 +61,7 @@ def _add_chance_level(
     return cast(Line2D, chance_level)
 
 
-class RocCurveDisplay(
-    StyleDisplayMixin, HelpDisplayMixin, _ClassifierCurveDisplayMixin, PlotBackendMixin
-):
+class RocCurveDisplay(_ClassifierCurveDisplayMixin, DisplayMixin):
     """ROC Curve visualization.
 
     An instance of this class should be created by `EstimatorReport.metrics.roc()`.
@@ -703,8 +699,8 @@ def _plot_comparison_cross_validation(
 
         return self.ax_, lines, info_pos_label
 
-    @StyleDisplayMixin.style_plot
-    def _plot_matplotlib(
+    @DisplayMixin.style_plot
+    def plot(
         self,
         *,
         estimator_name: str | None = None,
@@ -750,6 +746,24 @@ def _plot_matplotlib(
         >>> display = report.metrics.roc()
         >>> display.plot(roc_curve_kwargs={"color": "tab:red"})
         """
+        return self._plot(
+            estimator_name=estimator_name,
+            roc_curve_kwargs=roc_curve_kwargs,
+            plot_chance_level=plot_chance_level,
+            chance_level_kwargs=chance_level_kwargs,
+            despine=despine,
+        )
+
+    def _plot_matplotlib(
+        self,
+        *,
+        estimator_name: str | None = None,
+        roc_curve_kwargs: dict[str, Any] | list[dict[str, Any]] | None = None,
+        plot_chance_level: bool = True,
+        chance_level_kwargs: dict[str, Any] | None = None,
+        despine: bool = True,
+    ) -> None:
+        """Matplotlib implementation of the `plot` method."""
         if (
             self.report_type == "comparison-cross-validation"
             and self.ml_task == "multiclass-classification"
diff --git a/skore/src/skore/_sklearn/_plot/style.py b/skore/src/skore/_sklearn/_plot/style.py
deleted file mode 100644
index cdb2a4646e..0000000000
--- a/skore/src/skore/_sklearn/_plot/style.py
+++ /dev/null
@@ -1,115 +0,0 @@
-from collections.abc import Callable
-from functools import wraps
-from typing import Any
-
-import matplotlib.pyplot as plt
-
-DEFAULT_STYLE = {
-    "font.size": 14,
-    "axes.labelsize": 14,
-    "axes.titlesize": 14,
-    "xtick.labelsize": 13,
-    "ytick.labelsize": 13,
-    "legend.fontsize": 10,
-    "legend.title_fontsize": 11,
-    "axes.linewidth": 1.25,
-    "grid.linewidth": 1.25,
-    "lines.linewidth": 1.75,
-    "lines.markersize": 6,
-    "patch.linewidth": 1.25,
-    "xtick.major.width": 1.5,
-    "ytick.major.width": 1.5,
-    "xtick.minor.width": 1.25,
-    "ytick.minor.width": 1.25,
-    "xtick.major.size": 7,
-    "ytick.major.size": 7,
-    "xtick.minor.size": 5,
-    "ytick.minor.size": 5,
-    "legend.loc": "upper left",
-    "legend.borderaxespad": 0,
-}
-
-
-class StyleDisplayMixin:
-    """Mixin to control the style plot of a display."""
-
-    @property
-    def _style_params(self) -> list[str]:
-        """Get the list of available style parameters.
-
-        Returns
-        -------
-        list
-            List of style parameter names (without '_default_' prefix).
-        """
-        prefix = "_default_"
-        suffix = "_kwargs"
-        return [
-            attr[len(prefix) :]
-            for attr in dir(self)
-            if attr.startswith(prefix) and attr.endswith(suffix)
-        ]
-
-    def set_style(self, **kwargs: Any):
-        """Set the style parameters for the display.
-
-        Parameters
-        ----------
-        **kwargs : dict
-            Style parameters to set. Each parameter name should correspond to a
-            a style attribute passed to the plot method of the display.
-
-        Returns
-        -------
-        self : object
-            Returns the instance itself.
-
-        Raises
-        ------
-        ValueError
-            If a style parameter is unknown.
-        """
-        for param_name, param_value in kwargs.items():
-            default_attr = f"_default_{param_name}"
-            if not hasattr(self, default_attr):
-                raise ValueError(
-                    f"Unknown style parameter: {param_name}. "
-                    f"The parameter name should be one of {self._style_params}."
-                )
-            setattr(self, default_attr, param_value)
-        return self
-
-    @staticmethod
-    def style_plot(plot_func: Callable) -> Callable:
-        """Apply consistent style to skore displays.
-
-        This decorator:
-        1. Applies default style settings
-        2. Executes `plot_func`
-        3. Applies `tight_layout`
-
-        Parameters
-        ----------
-        plot_func : callable
-            The plot function to be decorated.
-
-        Returns
-        -------
-        callable
-            The decorated plot function.
-        """
-
-        @wraps(plot_func)
-        def wrapper(self, *args: Any, **kwargs: Any) -> Any:
-            # We need to manually handle setting the style of the parameters because
-            # `plt.style.context` has a side effect with the interactive mode.
-            # See https://github.com/matplotlib/matplotlib/issues/25041
-            original_params = {key: plt.rcParams[key] for key in DEFAULT_STYLE}
-            plt.rcParams.update(DEFAULT_STYLE)
-            try:
-                result = plot_func(self, *args, **kwargs)
-            finally:
-                plt.rcParams.update(original_params)
-            return result
-
-        return wrapper
diff --git a/skore/src/skore/_sklearn/_plot/utils.py b/skore/src/skore/_sklearn/_plot/utils.py
index 37b31cf034..95bb4e9e84 100644
--- a/skore/src/skore/_sklearn/_plot/utils.py
+++ b/skore/src/skore/_sklearn/_plot/utils.py
@@ -1,6 +1,4 @@
-import inspect
 from collections.abc import Sequence
-from io import StringIO
 from typing import Any
 
 import numpy as np
@@ -8,18 +6,13 @@
 from matplotlib.axes import Axes
 from matplotlib.colors import Colormap
 from matplotlib.figure import Figure
-from rich.console import Console
-from rich.panel import Panel
-from rich.tree import Tree
 from sklearn.utils.validation import (
     _check_pos_label_consistency,
     check_consistent_length,
 )
 
-from skore._config import get_config
 from skore._sklearn.types import (
     MLTask,
-    PlotBackend,
     PositiveLabel,
     ReportType,
     YPlotData,
@@ -46,96 +39,6 @@
 ]
 
 
-class HelpDisplayMixin:
-    """Mixin class to add help functionality to a class."""
-
-    estimator_name: str  # defined in the concrete display class
-
-    def _get_attributes_for_help(self) -> list[str]:
-        """Get the attributes ending with '_' to display in help."""
-        attributes = []
-        for name in dir(self):
-            if name.endswith("_") and not name.startswith("_"):
-                attributes.append(f".{name}")
-        return sorted(attributes)
-
-    def _get_methods_for_help(self) -> list[tuple[str, Any]]:
-        """Get the public methods to display in help."""
-        methods = inspect.getmembers(self, predicate=inspect.ismethod)
-        filtered_methods = []
-        for name, method in methods:
-            is_private = name.startswith("_")
-            is_class_method = inspect.ismethod(method) and method.__self__ is type(self)
-            is_help_method = name == "help"
-            if not (is_private or is_class_method or is_help_method):
-                filtered_methods.append((f".{name}(...)", method))
-        return sorted(filtered_methods)
-
-    def _create_help_tree(self) -> Tree:
-        """Create a rich Tree with attributes and methods."""
-        tree = Tree("display")
-
-        attributes = self._get_attributes_for_help()
-        attr_branch = tree.add("[bold cyan] Attributes[/bold cyan]")
-        # Ensure figure_ and ax_ are first
-        sorted_attrs = sorted(attributes)
-        if ("figure_" in sorted_attrs) and ("ax_" in sorted_attrs):
-            sorted_attrs.remove(".ax_")
-            sorted_attrs.remove(".figure_")
-            sorted_attrs = [".figure_", ".ax_"] + [
-                attr for attr in sorted_attrs if attr not in [".figure_", ".ax_"]
-            ]
-        for attr in sorted_attrs:
-            attr_branch.add(attr)
-
-        methods = self._get_methods_for_help()
-        method_branch = tree.add("[bold cyan]Methods[/bold cyan]")
-        for name, method in methods:
-            description = (
-                method.__doc__.split("\n")[0]
-                if method.__doc__
-                else "No description available"
-            )
-            method_branch.add(f"{name} - {description}")
-
-        return tree
-
-    def _create_help_panel(self) -> Panel:
-        return Panel(
-            self._create_help_tree(),
-            title=f"[bold cyan]{self.__class__.__name__} [/bold cyan]",
-            border_style="orange1",
-            expand=False,
-        )
-
-    def help(self) -> None:
-        """Display available attributes and methods using rich."""
-        from skore import console  # avoid circular import
-
-        console.print(self._create_help_panel())
-
-    def __str__(self) -> str:
-        """Return a string representation using rich."""
-        string_buffer = StringIO()
-        console = Console(file=string_buffer, force_terminal=False)
-        console.print(
-            Panel(
-                "Get guidance using the help() method",
-                title=f"[cyan]{self.__class__.__name__}[/cyan]",
-                border_style="orange1",
-                expand=False,
-            )
-        )
-        return string_buffer.getvalue()
-
-    def __repr__(self) -> str:
-        """Return a string representation using rich."""
-        string_buffer = StringIO()
-        console = Console(file=string_buffer, force_terminal=False)
-        console.print(f"[cyan]skore.{self.__class__.__name__}(...)[/cyan]")
-        return string_buffer.getvalue()
-
-
 class _ClassifierCurveDisplayMixin:
     """Mixin class to be used in Displays requiring a binary classifier.
 
@@ -333,28 +236,6 @@ def _adjust_fig_size(
     fig.set_size_inches((width, height))
 
 
-class PlotBackendMixin:
-    """Mixin class for Displays to dispatch plotting to the configured backend."""
-
-    def plot(self, **kwargs):
-        """Show as a plot."""
-        plot_backend = get_config()["plot_backend"]
-        if plot_backend == "matplotlib":
-            return self._plot_matplotlib(**kwargs)
-        elif plot_backend == "plotly":
-            return self._plot_plotly(**kwargs)
-        else:
-            raise NotImplementedError(
-                f"Plotting backend {plot_backend} not available. "
-                f"Available options are {PlotBackend.__args__}."
-            )
-
-    def _plot_plotly(self, **kwargs):
-        raise NotImplementedError(
-            "Plotting with plotly is not supported for this Display."
-        )
-
-
 def _despine_matplotlib_axis(
     ax: Axes,
     *,
diff --git a/skore/tests/unit/displays/test_style.py b/skore/tests/unit/displays/test_style.py
index 7eb675abda..5357f0d31d 100644
--- a/skore/tests/unit/displays/test_style.py
+++ b/skore/tests/unit/displays/test_style.py
@@ -1,5 +1,5 @@
 import pytest
-from skore._sklearn._plot.style import StyleDisplayMixin
+from skore._sklearn._plot.base import StyleDisplayMixin
 
 
 class TestDisplay(StyleDisplayMixin):