diff --git a/sklearnex/_device_offload.py b/sklearnex/_device_offload.py
index 7e299f07e0..34893af44b 100644
--- a/sklearnex/_device_offload.py
+++ b/sklearnex/_device_offload.py
@@ -16,6 +16,7 @@
 
 from functools import wraps
 
+from daal4py.sklearn._utils import sklearn_check_version
 from onedal._device_offload import _copy_to_usm, _get_global_queue, _transfer_to_host
 from onedal.utils._array_api import _asarray
 from onedal.utils._dpep_helpers import dpnp_available
@@ -25,6 +26,7 @@
     from onedal.utils._array_api import _convert_to_dpnp
 
 from ._config import get_config
+from .utils import get_tags
 
 
 def _get_backend(obj, queue, method_name, *data):
@@ -59,33 +61,42 @@ def _get_backend(obj, queue, method_name, *data):
 
 def dispatch(obj, method_name, branches, *args, **kwargs):
     q = _get_global_queue()
+
+    array_api_offload = (
+        "array_api_dispatch" in get_config() and get_config()["array_api_dispatch"]
+    )
+
+    onedal_array_api = array_api_offload and get_tags(obj)["onedal_array_api"]
+    sklearn_array_api = array_api_offload and get_tags(obj)["array_api_support"]
+
+    # We need to avoid a copy to host here if zero_copy supported
+    backend = ""
+    if onedal_array_api:
+        backend, q, patching_status = _get_backend(obj, q, method_name, *args)
+        if backend == "onedal":
+            patching_status.write_log(queue=q, transferred_to_host=False)
+            return branches[backend](obj, *args, **kwargs, queue=q)
+        if sklearn_array_api and backend == "sklearn":
+            patching_status.write_log(transferred_to_host=False)
+            return branches[backend](obj, *args, **kwargs)
+
+    # move to host because it is necessary for checking
+    # we only guarantee onedal_cpu_supported and onedal_gpu_supported are generalized to non-numpy inputs
+    # for zero copy estimators. this will eventually be deprecated when all estimators are zero-copy generalized
     has_usm_data_for_args, q, hostargs = _transfer_to_host(q, *args)
     has_usm_data_for_kwargs, q, hostvalues = _transfer_to_host(q, *kwargs.values())
     hostkwargs = dict(zip(kwargs.keys(), hostvalues))
-
-    backend, q, patching_status = _get_backend(obj, q, method_name, *hostargs)
     has_usm_data = has_usm_data_for_args or has_usm_data_for_kwargs
+
+    if not backend:
+        backend, q, patching_status = _get_backend(obj, q, method_name, *hostargs)
+
     if backend == "onedal":
-        # Host args only used before onedal backend call.
-        # Device will be offloaded when onedal backend will be called.
         patching_status.write_log(queue=q, transferred_to_host=False)
         return branches[backend](obj, *hostargs, **hostkwargs, queue=q)
     if backend == "sklearn":
-        if (
-            "array_api_dispatch" in get_config()
-            and get_config()["array_api_dispatch"]
-            and "array_api_support" in obj._get_tags()
-            and obj._get_tags()["array_api_support"]
-            and not has_usm_data
-        ):
-            # USM ndarrays are also excluded for the fallback Array API. Currently, DPNP.ndarray is
-            # not compliant with the Array API standard, and DPCTL usm_ndarray Array API is compliant,
-            # except for the linalg module. There is no guarantee that stock scikit-learn will
-            # work with such input data. The condition will be updated after DPNP.ndarray and
-            # DPCTL usm_ndarray enabling for conformance testing and these arrays supportance
-            # of the fallback cases.
-            # If `array_api_dispatch` enabled and array api is supported for the stock scikit-learn,
-            # then raw inputs are used for the fallback.
+        if sklearn_array_api and not has_usm_data:
+            # dpnp fallback is not handled properly yet.
             patching_status.write_log(transferred_to_host=False)
             return branches[backend](obj, *args, **kwargs)
         else:
diff --git a/sklearnex/base.py b/sklearnex/base.py
new file mode 100644
index 0000000000..9d83f98f46
--- /dev/null
+++ b/sklearnex/base.py
@@ -0,0 +1,57 @@
+# ===============================================================================
+# Copyright contributors to the oneDAL project
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ===============================================================================
+
+from abc import ABC
+
+from daal4py.sklearn._utils import sklearn_check_version
+
+
+class IntelEstimator(ABC):
+
+    if sklearn_check_version("1.6"):
+        # Starting in sklearn 1.6, _more_tags is deprecated. An IntelEstimator
+        # is defined to handle the various versioning issues with the tags and
+        # with the ongoing rollout of sklearn's array_api support. This will make
+        # maintenance easier, and centralize tag changes to a single location.
+
+        def __sklearn_tags__(self):
+            tags = super().__sklearn_tags__()
+            tags.onedal_array_api = False
+            return tags
+
+    elif sklearn_check_version("1.3"):
+
+        def _more_tags(self):
+            return {"onedal_array_api": False}
+
+    else:
+        # array_api_support tag was added in sklearn 1.3 via scikit-learn/scikit-learn#26372
+        def _more_tags(self):
+            return {"array_api_support": False, "onedal_array_api": False}
+
+    if sklearn_check_version("1.4"):
+
+        def _get_doc_link(self) -> str:
+            # This method is meant to generate a clickable doc link for classses
+            # in sklearnex that are not part of base scikit-learn. It should be
+            # inherited before inheriting from a scikit-learn estimator, otherwise
+            # will get overriden by the estimator's original.
+            url = super()._get_doc_link()
+            if not url:
+                module_path, _ = self.__class__.__module__.rsplit(".", 1)
+                class_name = self.__class__.__name__
+                url = f"https://intel.github.io/scikit-learn-intelex/latest/non-scikit-algorithms.html#{module_path}.{class_name}"
+            return url
diff --git a/sklearnex/basic_statistics/basic_statistics.py b/sklearnex/basic_statistics/basic_statistics.py
index 26f78ac16e..0138783cc7 100644
--- a/sklearnex/basic_statistics/basic_statistics.py
+++ b/sklearnex/basic_statistics/basic_statistics.py
@@ -28,7 +28,8 @@
 from onedal.utils import _is_csr
 
 from .._device_offload import dispatch
-from .._utils import IntelEstimator, PatchingConditionsChain
+from .._utils import PatchingConditionsChain
+from ..base import IntelEstimator
 
 if sklearn_check_version("1.6"):
     from sklearn.utils.validation import validate_data
diff --git a/sklearnex/basic_statistics/incremental_basic_statistics.py b/sklearnex/basic_statistics/incremental_basic_statistics.py
index d1ddcd55dc..664d0fd811 100644
--- a/sklearnex/basic_statistics/incremental_basic_statistics.py
+++ b/sklearnex/basic_statistics/incremental_basic_statistics.py
@@ -26,7 +26,8 @@
 )
 
 from .._device_offload import dispatch
-from .._utils import IntelEstimator, PatchingConditionsChain
+from .._utils import PatchingConditionsChain
+from ..base import IntelEstimator
 
 if sklearn_check_version("1.2"):
     from sklearn.utils._param_validation import Interval, StrOptions
diff --git a/sklearnex/cluster/dbscan.py b/sklearnex/cluster/dbscan.py
index ef5f6b78d9..0d81eb1bd6 100755
--- a/sklearnex/cluster/dbscan.py
+++ b/sklearnex/cluster/dbscan.py
@@ -27,6 +27,7 @@
 
 from .._device_offload import dispatch
 from .._utils import PatchingConditionsChain
+from ..base import IntelEstimator
 
 if sklearn_check_version("1.1") and not sklearn_check_version("1.2"):
     from sklearn.utils import check_scalar
@@ -37,7 +38,7 @@
     validate_data = _sklearn_DBSCAN._validate_data
 
 
-class BaseDBSCAN(ABC):
+class BaseDBSCAN(IntelEstimator):
     def _onedal_dbscan(self, **onedal_params):
         return onedal_DBSCAN(**onedal_params)
 
@@ -51,7 +52,7 @@ def _save_attributes(self):
 
 
 @control_n_jobs(decorated_methods=["fit"])
-class DBSCAN(_sklearn_DBSCAN, BaseDBSCAN):
+class DBSCAN(BaseDBSCAN, _sklearn_DBSCAN):
     __doc__ = _sklearn_DBSCAN.__doc__
 
     if sklearn_check_version("1.2"):
diff --git a/sklearnex/cluster/k_means.py b/sklearnex/cluster/k_means.py
index 4ba75ca5b8..c092dae9c3 100644
--- a/sklearnex/cluster/k_means.py
+++ b/sklearnex/cluster/k_means.py
@@ -40,6 +40,7 @@
 
     from .._device_offload import dispatch, wrap_output_data
     from .._utils import PatchingConditionsChain
+    from ..base import IntelEstimator
 
     if sklearn_check_version("1.6"):
         from sklearn.utils.validation import validate_data
@@ -47,7 +48,7 @@
         validate_data = _sklearn_KMeans._validate_data
 
     @control_n_jobs(decorated_methods=["fit", "fit_transform", "predict", "score"])
-    class KMeans(_sklearn_KMeans):
+    class KMeans(IntelEstimator, _sklearn_KMeans):
         __doc__ = _sklearn_KMeans.__doc__
 
         if sklearn_check_version("1.2"):
diff --git a/sklearnex/covariance/incremental_covariance.py b/sklearnex/covariance/incremental_covariance.py
index 89ed92b601..4b9bf99563 100644
--- a/sklearnex/covariance/incremental_covariance.py
+++ b/sklearnex/covariance/incremental_covariance.py
@@ -33,7 +33,8 @@
 from sklearnex import config_context
 
 from .._device_offload import dispatch, wrap_output_data
-from .._utils import IntelEstimator, PatchingConditionsChain, register_hyperparameters
+from .._utils import PatchingConditionsChain, register_hyperparameters
+from ..base import IntelEstimator
 from ..metrics import pairwise_distances
 from ..utils._array_api import get_namespace
 
diff --git a/sklearnex/decomposition/pca.py b/sklearnex/decomposition/pca.py
index 143587aa16..3e5616d7ae 100755
--- a/sklearnex/decomposition/pca.py
+++ b/sklearnex/decomposition/pca.py
@@ -32,6 +32,7 @@
 
     from .._device_offload import dispatch, wrap_output_data
     from .._utils import PatchingConditionsChain
+    from ..base import IntelEstimator
     from ..utils._array_api import get_namespace
 
     if sklearn_check_version("1.1") and not sklearn_check_version("1.2"):
@@ -50,7 +51,7 @@
         validate_data = _sklearn_PCA._validate_data
 
     @control_n_jobs(decorated_methods=["fit", "transform", "fit_transform"])
-    class PCA(_sklearn_PCA):
+    class PCA(IntelEstimator, _sklearn_PCA):
         __doc__ = _sklearn_PCA.__doc__
 
         if sklearn_check_version("1.2"):
diff --git a/sklearnex/ensemble/_forest.py b/sklearnex/ensemble/_forest.py
index 2a04962645..bd485726b4 100644
--- a/sklearnex/ensemble/_forest.py
+++ b/sklearnex/ensemble/_forest.py
@@ -62,6 +62,7 @@
 
 from .._device_offload import dispatch, wrap_output_data
 from .._utils import PatchingConditionsChain
+from ..base import IntelEstimator
 from ..utils._array_api import get_namespace
 
 if sklearn_check_version("1.2"):
@@ -75,7 +76,7 @@
     validate_data = BaseEstimator._validate_data
 
 
-class BaseForest(ABC):
+class BaseForest(IntelEstimator):
     _onedal_factory = None
 
     def _onedal_fit(self, X, y, sample_weight=None, queue=None):
@@ -402,7 +403,7 @@ def base_estimator(self, estimator):
             self.estimator = estimator
 
 
-class ForestClassifier(_sklearn_ForestClassifier, BaseForest):
+class ForestClassifier(BaseForest, _sklearn_ForestClassifier):
     # Surprisingly, even though scikit-learn warns against using
     # their ForestClassifier directly, it actually has a more stable
     # API than the user-facing objects (over time). If they change it
@@ -851,7 +852,7 @@ def _onedal_score(self, X, y, sample_weight=None, queue=None):
         )
 
 
-class ForestRegressor(_sklearn_ForestRegressor, BaseForest):
+class ForestRegressor(BaseForest, _sklearn_ForestRegressor):
     _err = "out_of_bag_error_r2|out_of_bag_error_prediction"
     _get_tree_state = staticmethod(get_tree_state_reg)
 
diff --git a/sklearnex/linear_model/incremental_linear.py b/sklearnex/linear_model/incremental_linear.py
index 622b15ef6c..d79c59667a 100644
--- a/sklearnex/linear_model/incremental_linear.py
+++ b/sklearnex/linear_model/incremental_linear.py
@@ -40,7 +40,8 @@
 from onedal.common.hyperparameters import get_hyperparameters
 
 from .._device_offload import dispatch, wrap_output_data
-from .._utils import IntelEstimator, PatchingConditionsChain, register_hyperparameters
+from .._utils import PatchingConditionsChain, register_hyperparameters
+from ..base import IntelEstimator
 
 
 @register_hyperparameters(
diff --git a/sklearnex/linear_model/incremental_ridge.py b/sklearnex/linear_model/incremental_ridge.py
index 39097d3e8d..639f6eaf7c 100644
--- a/sklearnex/linear_model/incremental_ridge.py
+++ b/sklearnex/linear_model/incremental_ridge.py
@@ -33,6 +33,7 @@
 
 from .._device_offload import dispatch, wrap_output_data
 from .._utils import PatchingConditionsChain
+from ..base import IntelEstimator
 
 if sklearn_check_version("1.6"):
     from sklearn.utils.validation import validate_data
@@ -43,7 +44,7 @@
 @control_n_jobs(
     decorated_methods=["fit", "partial_fit", "predict", "score", "_onedal_finalize_fit"]
 )
-class IncrementalRidge(MultiOutputMixin, RegressorMixin, BaseEstimator):
+class IncrementalRidge(IntelEstimator, MultiOutputMixin, RegressorMixin, BaseEstimator):
     """
     Incremental estimator for Ridge Regression.
     Allows to train Ridge Regression if data is splitted into batches.
diff --git a/sklearnex/linear_model/linear.py b/sklearnex/linear_model/linear.py
index fb7eca8cf1..71f85d2a15 100644
--- a/sklearnex/linear_model/linear.py
+++ b/sklearnex/linear_model/linear.py
@@ -28,6 +28,7 @@
 from .._config import get_config
 from .._device_offload import dispatch, wrap_output_data
 from .._utils import PatchingConditionsChain, get_patch_message, register_hyperparameters
+from ..base import IntelEstimator
 
 if sklearn_check_version("1.0") and not sklearn_check_version("1.2"):
     from sklearn.linear_model._base import _deprecate_normalize
@@ -47,7 +48,7 @@
 
 @register_hyperparameters({"fit": get_hyperparameters("linear_regression", "train")})
 @control_n_jobs(decorated_methods=["fit", "predict", "score"])
-class LinearRegression(_sklearn_LinearRegression):
+class LinearRegression(IntelEstimator, _sklearn_LinearRegression):
     __doc__ = _sklearn_LinearRegression.__doc__
 
     if sklearn_check_version("1.2"):
diff --git a/sklearnex/linear_model/logistic_regression.py b/sklearnex/linear_model/logistic_regression.py
index 01e944c74f..be195aedb7 100644
--- a/sklearnex/linear_model/logistic_regression.py
+++ b/sklearnex/linear_model/logistic_regression.py
@@ -39,6 +39,7 @@
     from .._config import get_config
     from .._device_offload import dispatch, wrap_output_data
     from .._utils import PatchingConditionsChain, get_patch_message
+    from ..base import IntelEstimator
 
     if sklearn_check_version("1.6"):
         from sklearn.utils.validation import validate_data
@@ -47,7 +48,7 @@
 
     _sparsity_enabled = daal_check_version((2024, "P", 700))
 
-    class BaseLogisticRegression(ABC):
+    class BaseLogisticRegression(IntelEstimator):
         def _onedal_gpu_save_attributes(self):
             assert hasattr(self, "_onedal_estimator")
             self.classes_ = self._onedal_estimator.classes_
@@ -65,7 +66,7 @@ def _onedal_gpu_save_attributes(self):
             "score",
         ]
     )
-    class LogisticRegression(_sklearn_LogisticRegression, BaseLogisticRegression):
+    class LogisticRegression(BaseLogisticRegression, _sklearn_LogisticRegression):
         __doc__ = _sklearn_LogisticRegression.__doc__
 
         if sklearn_check_version("1.2"):
diff --git a/sklearnex/linear_model/ridge.py b/sklearnex/linear_model/ridge.py
index 85d6714905..74ff42cdbe 100644
--- a/sklearnex/linear_model/ridge.py
+++ b/sklearnex/linear_model/ridge.py
@@ -39,6 +39,7 @@
 
     from .._device_offload import dispatch, wrap_output_data
     from .._utils import PatchingConditionsChain
+    from ..base import IntelEstimator
 
     if sklearn_check_version("1.6"):
         from sklearn.utils.validation import validate_data
@@ -46,7 +47,7 @@
         validate_data = _sklearn_Ridge._validate_data
 
     @control_n_jobs(decorated_methods=["fit", "predict", "score"])
-    class Ridge(_sklearn_Ridge):
+    class Ridge(IntelEstimator, _sklearn_Ridge):
         __doc__ = _sklearn_Ridge.__doc__
 
         if sklearn_check_version("1.2"):
diff --git a/sklearnex/neighbors/common.py b/sklearnex/neighbors/common.py
index 0ad5a62dd1..d405b2c28f 100644
--- a/sklearnex/neighbors/common.py
+++ b/sklearnex/neighbors/common.py
@@ -28,10 +28,11 @@
 from onedal.utils import _check_array, _num_features, _num_samples
 
 from .._utils import PatchingConditionsChain
+from ..base import IntelEstimator
 from ..utils._array_api import get_namespace
 
 
-class KNeighborsDispatchingBase:
+class KNeighborsDispatchingBase(IntelEstimator):
     def _fit_validation(self, X, y=None):
         if sklearn_check_version("1.2"):
             self._validate_params()
diff --git a/sklearnex/neighbors/knn_classification.py b/sklearnex/neighbors/knn_classification.py
index 3b9871b4cf..2c7d6e5a79 100755
--- a/sklearnex/neighbors/knn_classification.py
+++ b/sklearnex/neighbors/knn_classification.py
@@ -26,6 +26,7 @@
 from onedal.neighbors import KNeighborsClassifier as onedal_KNeighborsClassifier
 
 from .._device_offload import dispatch, wrap_output_data
+from ..utils import get_tags
 from .common import KNeighborsDispatchingBase
 
 if sklearn_check_version("1.6"):
@@ -184,7 +185,7 @@ def _onedal_fit(self, X, y, queue=None):
         }
 
         try:
-            requires_y = self._get_tags()["requires_y"]
+            requires_y = get_tags(self)["requires_y"]
         except KeyError:
             requires_y = False
 
diff --git a/sklearnex/neighbors/knn_regression.py b/sklearnex/neighbors/knn_regression.py
index 5889687498..1112eb7f16 100755
--- a/sklearnex/neighbors/knn_regression.py
+++ b/sklearnex/neighbors/knn_regression.py
@@ -25,6 +25,7 @@
 from onedal.neighbors import KNeighborsRegressor as onedal_KNeighborsRegressor
 
 from .._device_offload import dispatch, wrap_output_data
+from ..utils import get_tags
 from .common import KNeighborsDispatchingBase
 
 if sklearn_check_version("1.6"):
@@ -166,7 +167,7 @@ def _onedal_fit(self, X, y, queue=None):
         }
 
         try:
-            requires_y = self._get_tags()["requires_y"]
+            requires_y = get_tags(self)["requires_y"]
         except KeyError:
             requires_y = False
 
diff --git a/sklearnex/neighbors/knn_unsupervised.py b/sklearnex/neighbors/knn_unsupervised.py
index d76e220cae..fa32b9cb7f 100755
--- a/sklearnex/neighbors/knn_unsupervised.py
+++ b/sklearnex/neighbors/knn_unsupervised.py
@@ -22,6 +22,7 @@
 from onedal.neighbors import NearestNeighbors as onedal_NearestNeighbors
 
 from .._device_offload import dispatch, wrap_output_data
+from ..utils import get_tags
 from .common import KNeighborsDispatchingBase
 
 if sklearn_check_version("1.6"):
@@ -140,7 +141,7 @@ def _onedal_fit(self, X, y=None, queue=None):
         }
 
         try:
-            requires_y = self._get_tags()["requires_y"]
+            requires_y = get_tags(self)["requires_y"]
         except KeyError:
             requires_y = False
 
diff --git a/sklearnex/preview/covariance/covariance.py b/sklearnex/preview/covariance/covariance.py
index 04bdc0be8d..668a6e617a 100644
--- a/sklearnex/preview/covariance/covariance.py
+++ b/sklearnex/preview/covariance/covariance.py
@@ -30,6 +30,7 @@
 
 from ..._device_offload import dispatch, wrap_output_data
 from ..._utils import PatchingConditionsChain, register_hyperparameters
+from ...base import IntelEstimator
 
 if sklearn_check_version("1.6"):
     from sklearn.utils.validation import validate_data
@@ -39,7 +40,7 @@
 
 @register_hyperparameters({"fit": get_hyperparameters("covariance", "compute")})
 @control_n_jobs(decorated_methods=["fit", "mahalanobis"])
-class EmpiricalCovariance(_sklearn_EmpiricalCovariance):
+class EmpiricalCovariance(IntelEstimator, _sklearn_EmpiricalCovariance):
     __doc__ = _sklearn_EmpiricalCovariance.__doc__
 
     if sklearn_check_version("1.2"):
diff --git a/sklearnex/preview/decomposition/incremental_pca.py b/sklearnex/preview/decomposition/incremental_pca.py
index fdf13e0817..51aeaa0c51 100644
--- a/sklearnex/preview/decomposition/incremental_pca.py
+++ b/sklearnex/preview/decomposition/incremental_pca.py
@@ -24,6 +24,7 @@
 
 from ..._device_offload import dispatch, wrap_output_data
 from ..._utils import PatchingConditionsChain
+from ...base import IntelEstimator
 
 if sklearn_check_version("1.6"):
     from sklearn.utils.validation import validate_data
@@ -34,7 +35,7 @@
 @control_n_jobs(
     decorated_methods=["fit", "partial_fit", "transform", "_onedal_finalize_fit"]
 )
-class IncrementalPCA(_sklearn_IncrementalPCA):
+class IncrementalPCA(IntelEstimator, _sklearn_IncrementalPCA):
 
     def __init__(self, n_components=None, *, whiten=False, copy=True, batch_size=None):
         super().__init__(
diff --git a/sklearnex/svm/_common.py b/sklearnex/svm/_common.py
index 4b481314ae..0971b405d1 100644
--- a/sklearnex/svm/_common.py
+++ b/sklearnex/svm/_common.py
@@ -30,6 +30,8 @@
 
 from .._config import config_context, get_config
 from .._utils import PatchingConditionsChain
+from ..base import IntelEstimator
+from ..utils import get_tags
 
 if sklearn_check_version("1.6"):
     from sklearn.utils.validation import validate_data
@@ -37,7 +39,7 @@
     validate_data = BaseEstimator._validate_data
 
 
-class BaseSVM(BaseEstimator, ABC):
+class BaseSVM(IntelEstimator):
 
     @property
     def _dual_coef_(self):
@@ -156,7 +158,7 @@ def _onedal_fit_checks(self, X, y, sample_weight=None):
                 )
 
         if y is None:
-            if self._get_tags()["requires_y"]:
+            if get_tags(self)["requires_y"]:
                 raise ValueError(
                     f"This {self.__class__.__name__} estimator "
                     f"requires y to be passed, but the target y is None."
diff --git a/sklearnex/svm/nusvc.py b/sklearnex/svm/nusvc.py
index 301d90ccc4..fa9f77295d 100644
--- a/sklearnex/svm/nusvc.py
+++ b/sklearnex/svm/nusvc.py
@@ -39,13 +39,13 @@
 if sklearn_check_version("1.6"):
     from sklearn.utils.validation import validate_data
 else:
-    validate_data = BaseSVC._validate_data
+    validate_data = _sklearn_NuSVC._validate_data
 
 
 @control_n_jobs(
     decorated_methods=["fit", "predict", "_predict_proba", "decision_function", "score"]
 )
-class NuSVC(_sklearn_NuSVC, BaseSVC):
+class NuSVC(BaseSVC, _sklearn_NuSVC):
     __doc__ = _sklearn_NuSVC.__doc__
 
     if sklearn_check_version("1.2"):
diff --git a/sklearnex/svm/nusvr.py b/sklearnex/svm/nusvr.py
index 6c746174ac..4979fde0a3 100644
--- a/sklearnex/svm/nusvr.py
+++ b/sklearnex/svm/nusvr.py
@@ -32,11 +32,11 @@
 if sklearn_check_version("1.6"):
     from sklearn.utils.validation import validate_data
 else:
-    validate_data = BaseSVR._validate_data
+    validate_data = _sklearn_NuSVR._validate_data
 
 
 @control_n_jobs(decorated_methods=["fit", "predict", "score"])
-class NuSVR(_sklearn_NuSVR, BaseSVR):
+class NuSVR(BaseSVR, _sklearn_NuSVR):
     __doc__ = _sklearn_NuSVR.__doc__
 
     if sklearn_check_version("1.2"):
diff --git a/sklearnex/svm/svc.py b/sklearnex/svm/svc.py
index bf5e7f32fc..8e8ced9eee 100644
--- a/sklearnex/svm/svc.py
+++ b/sklearnex/svm/svc.py
@@ -41,13 +41,13 @@
 if sklearn_check_version("1.6"):
     from sklearn.utils.validation import validate_data
 else:
-    validate_data = BaseSVC._validate_data
+    validate_data = _sklearn_SVC._validate_data
 
 
 @control_n_jobs(
     decorated_methods=["fit", "predict", "_predict_proba", "decision_function", "score"]
 )
-class SVC(_sklearn_SVC, BaseSVC):
+class SVC(BaseSVC, _sklearn_SVC):
     __doc__ = _sklearn_SVC.__doc__
 
     if sklearn_check_version("1.2"):
diff --git a/sklearnex/svm/svr.py b/sklearnex/svm/svr.py
index ff2641bea0..fdf1d5d7c7 100644
--- a/sklearnex/svm/svr.py
+++ b/sklearnex/svm/svr.py
@@ -28,11 +28,11 @@
 if sklearn_check_version("1.6"):
     from sklearn.utils.validation import validate_data
 else:
-    validate_data = BaseSVR._validate_data
+    validate_data = _sklearn_SVR._validate_data
 
 
 @control_n_jobs(decorated_methods=["fit", "predict", "score"])
-class SVR(_sklearn_SVR, BaseSVR):
+class SVR(BaseSVR, _sklearn_SVR):
     __doc__ = _sklearn_SVR.__doc__
 
     if sklearn_check_version("1.2"):
diff --git a/sklearnex/utils/__init__.py b/sklearnex/utils/__init__.py
index 686e089adf..0d30f6074b 100755
--- a/sklearnex/utils/__init__.py
+++ b/sklearnex/utils/__init__.py
@@ -14,6 +14,23 @@
 # limitations under the License.
 # ===============================================================================
 
+from daal4py.sklearn._utils import sklearn_check_version
+
 from .validation import assert_all_finite
 
-__all__ = ["assert_all_finite"]
+# Not an ideal solution, but this converts the outputs of newer sklearnex tags
+# into dicts to match how tags had been used. Someone more clever than me will
+# have to find a way of converting older tags into newer ones instead (with
+# minimal impact on performance).
+
+if sklearn_check_version("1.6"):
+    from sklearn.utils import get_tags as _sklearn_get_tags
+
+    get_tags = lambda estimator: _sklearn_get_tags(estimator).__dict__
+
+else:
+    from sklearn.base import BaseEstimator
+
+    get_tags = BaseEstimator._get_tags
+
+__all__ = ["assert_all_finite", "get_tags"]
diff --git a/sklearnex/utils/tests/test_array_api.py b/sklearnex/utils/tests/test_array_api.py
new file mode 100644
index 0000000000..ed929c4c34
--- /dev/null
+++ b/sklearnex/utils/tests/test_array_api.py
@@ -0,0 +1,60 @@
+# ==============================================================================
+# Copyright 2024 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+import numpy as np
+import pytest
+from numpy.testing import assert_allclose
+
+from daal4py.sklearn._utils import sklearn_check_version
+from onedal.tests.utils._dataframes_support import (
+    _convert_to_dataframe,
+    get_dataframes_and_queues,
+)
+
+# TODO:
+# add test suit for dpctl.tensor, dpnp.ndarray, numpy.ndarray without config_context(array_api_dispatch=True)).
+# TODO:
+# extend for DPNP inputs.
+
+
+@pytest.mark.skipif(
+    not sklearn_check_version("1.4"),
+    reason="Array API dispatch requires sklearn 1.4 version",
+)
+@pytest.mark.parametrize(
+    "dataframe,queue",
+    get_dataframes_and_queues(
+        dataframe_filter_="numpy,dpctl,array_api", device_filter_="cpu,gpu"
+    ),
+)
+def test_get_namespace_with_config_context(dataframe, queue):
+    """Test `get_namespace` with `array_api_dispatch` enabled."""
+    from sklearnex import config_context
+    from sklearnex.utils._array_api import get_namespace
+
+    array_api_compat = pytest.importorskip("array_api_compat")
+
+    X_np = np.asarray([[1, 2, 3]])
+    X = _convert_to_dataframe(X_np, sycl_queue=queue, target_df=dataframe)
+
+    with config_context(array_api_dispatch=True):
+        xp_out, is_array_api_compliant = get_namespace(X)
+        assert is_array_api_compliant
+        if not dataframe in "numpy,array_api":
+            # Rather than array_api_compat.get_namespace raw output
+            # `get_namespace` has specific wrapper classes for `numpy.ndarray`
+            # or `array-api-strict`.
+            assert xp_out == array_api_compat.get_namespace(X)