diff --git a/sklearn/preprocessing/_label.py b/sklearn/preprocessing/_label.py
index 43b6ac642284c..1d95ec8e97eb5 100644
--- a/sklearn/preprocessing/_label.py
+++ b/sklearn/preprocessing/_label.py
@@ -109,13 +109,23 @@ def _encode(values, uniques=None, encode=False, check_unknown=True):
     """
     if values.dtype == object:
         try:
-            res = _encode_python(values, uniques, encode)
+            return _encode_python(values, uniques, encode)
         except TypeError:
-            types = sorted(t.__qualname__
-                           for t in set(type(v) for v in values))
-            raise TypeError("Encoders require their input to be uniformly "
-                            f"strings or numbers. Got {types}")
-        return res
+            unique_types = set(type(v) for v in values)
+            type_names = sorted(t.__qualname__ for t in unique_types)
+            error_msg = ("Encoders require their input to be uniformly "
+                         f"strings or numbers. Got: {type_names}.")
+            if unique_types == {str, type(None)}:
+                # Be extra helpful to the user for this specific yet common
+                # case:
+                error_msg += (
+                    '\nPreprocess the features with sklearn.impute.'
+                    'SimpleImputer(strategy="constant", missing_values=None, '
+                    'fill_value="missing") to deal with None-encoded '
+                    'missing values in other-wise string encoded categorical'
+                    'data.'
+                )
+            raise TypeError(error_msg)
     else:
         return _encode_numpy(values, uniques, encode,
                              check_unknown=check_unknown)
diff --git a/sklearn/preprocessing/tests/test_encoders.py b/sklearn/preprocessing/tests/test_encoders.py
index 126a923a952dd..8ebe3547630a9 100644
--- a/sklearn/preprocessing/tests/test_encoders.py
+++ b/sklearn/preprocessing/tests/test_encoders.py
@@ -692,7 +692,19 @@ def test_encoders_has_categorical_tags(Encoder):
 
 @pytest.mark.parametrize('Encoder', [OneHotEncoder, OrdinalEncoder])
 def test_encoders_does_not_support_none_values(Encoder):
+    values = [["a"], [1]]
+    expected_msg = (
+        r"Encoders require their input to be uniformly strings or numbers\. "
+        r"Got: \['int', 'str'\]\."
+    )
+    with pytest.raises(TypeError, match=expected_msg):
+        Encoder().fit(values)
+
+    # Special case with extra missing value imputation suggestion:
     values = [["a"], [None]]
-    with pytest.raises(TypeError, match="Encoders require their input to be "
-                                        "uniformly strings or numbers."):
+    expected_msg = (
+        r'SimpleImputer\(strategy="constant", missing_values=None, '
+        r'fill_value="missing"\)'
+    )
+    with pytest.raises(TypeError, match=expected_msg):
         Encoder().fit(values)