diff --git a/sklearn/ensemble/tests/test_stacking.py b/sklearn/ensemble/tests/test_stacking.py
index 88dd76cb0b49d..d6b4c385b9073 100644
--- a/sklearn/ensemble/tests/test_stacking.py
+++ b/sklearn/ensemble/tests/test_stacking.py
@@ -27,8 +27,6 @@
 from sklearn.svm import LinearSVC
 from sklearn.svm import LinearSVR
 from sklearn.svm import SVC
-from sklearn.tree import DecisionTreeClassifier
-from sklearn.tree import DecisionTreeRegressor
 from sklearn.ensemble import RandomForestClassifier
 from sklearn.ensemble import RandomForestRegressor
 from sklearn.preprocessing import scale
@@ -44,8 +42,6 @@
 from sklearn.utils._testing import assert_allclose
 from sklearn.utils._testing import assert_allclose_dense_sparse
 from sklearn.utils._testing import ignore_warnings
-from sklearn.utils.estimator_checks import check_estimator
-from sklearn.utils.estimator_checks import check_no_attributes_set_in_init
 
 X_diabetes, y_diabetes = load_diabetes(return_X_y=True)
 X_iris, y_iris = load_iris(return_X_y=True)
@@ -368,24 +364,6 @@ def test_stacking_randomness(estimator, X, y):
     )
 
 
-# These warnings are raised due to _BaseComposition
-@pytest.mark.filterwarnings("ignore:TypeError occurred during set_params")
-@pytest.mark.filterwarnings("ignore:Estimator's parameters changed after")
-@pytest.mark.parametrize(
-    "estimator",
-    [StackingClassifier(
-        estimators=[('lr', LogisticRegression(random_state=0)),
-                    ('tree', DecisionTreeClassifier(random_state=0))]),
-     StackingRegressor(
-         estimators=[('lr', LinearRegression()),
-                     ('tree', DecisionTreeRegressor(random_state=0))])],
-    ids=['StackingClassifier', 'StackingRegressor']
-)
-def test_check_estimators_stacking_estimator(estimator):
-    check_estimator(estimator)
-    check_no_attributes_set_in_init(estimator.__class__.__name__, estimator)
-
-
 def test_stacking_classifier_stratify_default():
     # check that we stratify the classes for the default CV
     clf = StackingClassifier(
diff --git a/sklearn/ensemble/tests/test_voting.py b/sklearn/ensemble/tests/test_voting.py
index 7e8852f2d0f19..7fa1fce0f755d 100644
--- a/sklearn/ensemble/tests/test_voting.py
+++ b/sklearn/ensemble/tests/test_voting.py
@@ -7,8 +7,6 @@
 from sklearn.utils._testing import assert_almost_equal, assert_array_equal
 from sklearn.utils._testing import assert_array_almost_equal
 from sklearn.utils._testing import assert_raise_message
-from sklearn.utils.estimator_checks import check_estimator
-from sklearn.utils.estimator_checks import check_no_attributes_set_in_init
 from sklearn.exceptions import NotFittedError
 from sklearn.linear_model import LinearRegression
 from sklearn.linear_model import LogisticRegression
@@ -490,23 +488,6 @@ def test_none_estimator_with_weights(X, y, voter):
     assert y_pred.shape == y.shape
 
 
-@pytest.mark.parametrize(
-    "estimator",
-    [VotingRegressor(
-        estimators=[('lr', LinearRegression()),
-                    ('tree', DecisionTreeRegressor(random_state=0))]),
-     VotingClassifier(
-         estimators=[('lr', LogisticRegression(random_state=0)),
-                     ('tree', DecisionTreeClassifier(random_state=0))])],
-    ids=['VotingRegressor', 'VotingClassifier']
-)
-def test_check_estimators_voting_estimator(estimator):
-    # FIXME: to be removed when meta-estimators can specified themselves
-    # their testing parameters (for required parameters).
-    check_estimator(estimator)
-    check_no_attributes_set_in_init(estimator.__class__.__name__, estimator)
-
-
 @pytest.mark.parametrize(
     "est",
     [VotingRegressor(
diff --git a/sklearn/utils/estimator_checks.py b/sklearn/utils/estimator_checks.py
index 5b99e8e56c420..40a92fe63ab00 100644
--- a/sklearn/utils/estimator_checks.py
+++ b/sklearn/utils/estimator_checks.py
@@ -26,7 +26,9 @@
 from ._testing import create_memmap_backed_data
 from ._testing import raises
 from . import is_scalar_nan
+
 from ..discriminant_analysis import LinearDiscriminantAnalysis
+from ..linear_model import LogisticRegression
 from ..linear_model import Ridge
 
 from ..base import (
@@ -343,10 +345,24 @@ def _construct_instance(Estimator):
                 estimator = Estimator(Ridge())
             else:
                 estimator = Estimator(LinearDiscriminantAnalysis())
+        elif required_parameters in (['estimators'],):
+            # Heterogeneous ensemble classes (i.e. stacking, voting)
+            if issubclass(Estimator, RegressorMixin):
+                estimator = Estimator(estimators=[
+                    ("est1", Ridge(alpha=0.1)),
+                    ("est2", Ridge(alpha=1))
+                ])
+            else:
+                estimator = Estimator(estimators=[
+                    ("est1", LogisticRegression(C=0.1)),
+                    ("est2", LogisticRegression(C=1))
+                ])
         else:
-            raise SkipTest("Can't instantiate estimator {} which requires "
-                           "parameters {}".format(Estimator.__name__,
-                                                  required_parameters))
+            msg = (f"Can't instantiate estimator {Estimator.__name__} "
+                   f"parameters {required_parameters}")
+            # raise additional warning to be shown by pytest
+            warnings.warn(msg, SkipTestWarning)
+            raise SkipTest(msg)
     else:
         estimator = Estimator()
     return estimator