diff --git a/sklearn/ensemble/tests/test_gradient_boosting.py b/sklearn/ensemble/tests/test_gradient_boosting.py
index a28c69d0f7cc5..0c7f07929e370 100644
--- a/sklearn/ensemble/tests/test_gradient_boosting.py
+++ b/sklearn/ensemble/tests/test_gradient_boosting.py
@@ -345,10 +345,6 @@ def test_check_inputs():
     clf = GradientBoostingClassifier(n_estimators=100, random_state=1)
     assert_raises(ValueError, clf.fit, X, y + [0, 1])
 
-    clf = GradientBoostingClassifier(n_estimators=100, random_state=1)
-    assert_raises(ValueError, clf.fit, X, y,
-                  sample_weight=([1] * len(y)) + [0, 1])
-
     weight = [0, 0, 0, 1, 1, 1]
     clf = GradientBoostingClassifier(n_estimators=100, random_state=1)
     msg = ("y contains 1 class after sample_weight trimmed classes with "
diff --git a/sklearn/neighbors/tests/test_kde.py b/sklearn/neighbors/tests/test_kde.py
index 69aca8e8f75b8..6687cfa475ce8 100644
--- a/sklearn/neighbors/tests/test_kde.py
+++ b/sklearn/neighbors/tests/test_kde.py
@@ -210,10 +210,6 @@ def test_sample_weight_invalid():
     kde = KernelDensity()
     data = np.reshape([1., 2., 3.], (-1, 1))
 
-    sample_weight = [0.1, 0.2]
-    with pytest.raises(ValueError):
-        kde.fit(data, sample_weight=sample_weight)
-
     sample_weight = [0.1, -0.2, 0.3]
     expected_err = "sample_weight must have positive values"
     with pytest.raises(ValueError, match=expected_err):
diff --git a/sklearn/svm/tests/test_svm.py b/sklearn/svm/tests/test_svm.py
index 191420d1d7147..fb811940c2971 100644
--- a/sklearn/svm/tests/test_svm.py
+++ b/sklearn/svm/tests/test_svm.py
@@ -639,11 +639,6 @@ def test_bad_input():
     with pytest.raises(ValueError):
         clf.fit(X, Y)
 
-    # sample_weight bad dimensions
-    clf = svm.SVC()
-    with pytest.raises(ValueError):
-        clf.fit(X, Y, sample_weight=range(len(X) - 1))
-
     # predict with sparse input when trained with dense
     clf = svm.SVC().fit(X, Y)
     with pytest.raises(ValueError):
diff --git a/sklearn/tree/tests/test_tree.py b/sklearn/tree/tests/test_tree.py
index 572bf8d01d57c..1149ceb8678d9 100644
--- a/sklearn/tree/tests/test_tree.py
+++ b/sklearn/tree/tests/test_tree.py
@@ -1125,14 +1125,6 @@ def test_sample_weight_invalid():
     with pytest.raises(TypeError, match=expected_err):
         clf.fit(X, y, sample_weight=sample_weight)
 
-    sample_weight = np.ones(101)
-    with pytest.raises(ValueError):
-        clf.fit(X, y, sample_weight=sample_weight)
-
-    sample_weight = np.ones(99)
-    with pytest.raises(ValueError):
-        clf.fit(X, y, sample_weight=sample_weight)
-
 
 def check_class_weights(name):
     """Check class_weights resemble sample_weights behavior."""
diff --git a/sklearn/utils/estimator_checks.py b/sklearn/utils/estimator_checks.py
index ef9a4b1ca17f6..1e86f68d4ca3c 100644
--- a/sklearn/utils/estimator_checks.py
+++ b/sklearn/utils/estimator_checks.py
@@ -80,6 +80,7 @@ def _yield_checks(name, estimator):
     yield check_sample_weights_pandas_series
     yield check_sample_weights_not_an_array
     yield check_sample_weights_list
+    yield check_sample_weights_shape
     yield check_sample_weights_invariance
     yield check_estimators_fit_returns_self
     yield partial(check_estimators_fit_returns_self, readonly_memmap=True)
@@ -764,6 +765,31 @@ def check_sample_weights_list(name, estimator_orig):
         estimator.fit(X, y, sample_weight=sample_weight)
 
 
+@ignore_warnings(category=FutureWarning)
+def check_sample_weights_shape(name, estimator_orig):
+    # check that estimators raise an error if sample_weight
+    # shape mismatches the input
+    if (has_fit_parameter(estimator_orig, "sample_weight") and
+            not (hasattr(estimator_orig, "_pairwise")
+                 and estimator_orig._pairwise)):
+        estimator = clone(estimator_orig)
+        X = np.array([[1, 3], [1, 3], [1, 3], [1, 3],
+                      [2, 1], [2, 1], [2, 1], [2, 1],
+                      [3, 3], [3, 3], [3, 3], [3, 3],
+                      [4, 1], [4, 1], [4, 1], [4, 1]])
+        y = np.array([1, 1, 1, 1, 2, 2, 2, 2,
+                      1, 1, 1, 1, 2, 2, 2, 2])
+        y = _enforce_estimator_tags_y(estimator, y)
+
+        estimator.fit(X, y, sample_weight=np.ones(len(y)))
+
+        assert_raises(ValueError, estimator.fit, X, y,
+                      sample_weight=np.ones(2*len(y)))
+
+        assert_raises(ValueError, estimator.fit, X, y,
+                      sample_weight=np.ones((len(y), 2)))
+
+
 @ignore_warnings(category=FutureWarning)
 def check_sample_weights_invariance(name, estimator_orig):
     # check that the estimators yield same results for