diff --git a/maint_tools/test_docstrings.py b/maint_tools/test_docstrings.py
index cba5e8dfd2900..c5dbbed17847e 100644
--- a/maint_tools/test_docstrings.py
+++ b/maint_tools/test_docstrings.py
@@ -30,7 +30,6 @@
     "ColumnTransformer",
     "ComplementNB",
     "CountVectorizer",
-    "DBSCAN",
     "DecisionTreeClassifier",
     "DecisionTreeRegressor",
     "DictVectorizer",
diff --git a/sklearn/cluster/_dbscan.py b/sklearn/cluster/_dbscan.py
index 097202759ba90..35c17bbae061a 100644
--- a/sklearn/cluster/_dbscan.py
+++ b/sklearn/cluster/_dbscan.py
@@ -183,7 +183,7 @@ class DBSCAN(ClusterMixin, BaseEstimator):
         The number of samples (or total weight) in a neighborhood for a point
         to be considered as a core point. This includes the point itself.
 
-    metric : string, or callable, default='euclidean'
+    metric : str, or callable, default='euclidean'
         The metric to use when calculating distance between instances in a
         feature array. If metric is a string or callable, it must be one of
         the options allowed by :func:`sklearn.metrics.pairwise_distances` for
@@ -239,18 +239,6 @@ class DBSCAN(ClusterMixin, BaseEstimator):
 
         .. versionadded:: 0.24
 
-    Examples
-    --------
-    >>> from sklearn.cluster import DBSCAN
-    >>> import numpy as np
-    >>> X = np.array([[1, 2], [2, 2], [2, 3],
-    ...               [8, 7], [8, 8], [25, 80]])
-    >>> clustering = DBSCAN(eps=3, min_samples=2).fit(X)
-    >>> clustering.labels_
-    array([ 0,  0,  0,  1,  1, -1])
-    >>> clustering
-    DBSCAN(eps=3, min_samples=2)
-
     See Also
     --------
     OPTICS : A similar clustering at multiple values of eps. Our implementation
@@ -289,6 +277,18 @@ class DBSCAN(ClusterMixin, BaseEstimator):
     Schubert, E., Sander, J., Ester, M., Kriegel, H. P., & Xu, X. (2017).
     DBSCAN revisited, revisited: why and how you should (still) use DBSCAN.
     ACM Transactions on Database Systems (TODS), 42(3), 19.
+
+    Examples
+    --------
+    >>> from sklearn.cluster import DBSCAN
+    >>> import numpy as np
+    >>> X = np.array([[1, 2], [2, 2], [2, 3],
+    ...               [8, 7], [8, 8], [25, 80]])
+    >>> clustering = DBSCAN(eps=3, min_samples=2).fit(X)
+    >>> clustering.labels_
+    array([ 0,  0,  0,  1,  1, -1])
+    >>> clustering
+    DBSCAN(eps=3, min_samples=2)
     """
 
     def __init__(
@@ -323,19 +323,19 @@ def fit(self, X, y=None, sample_weight=None):
             ``metric='precomputed'``. If a sparse matrix is provided, it will
             be converted into a sparse ``csr_matrix``.
 
+        y : Ignored
+            Not used, present here for API consistency by convention.
+
         sample_weight : array-like of shape (n_samples,), default=None
             Weight of each sample, such that a sample with a weight of at least
             ``min_samples`` is by itself a core sample; a sample with a
             negative weight may inhibit its eps-neighbor from being core.
             Note that weights are absolute, and default to 1.
 
-        y : Ignored
-            Not used, present here for API consistency by convention.
-
         Returns
         -------
-        self
-
+        self : object
+            Returns a fitted instance of self.
         """
         X = self._validate_data(X, accept_sparse="csr")
 
@@ -394,8 +394,7 @@ def fit(self, X, y=None, sample_weight=None):
         return self
 
     def fit_predict(self, X, y=None, sample_weight=None):
-        """Perform DBSCAN clustering from features or distance matrix,
-        and return cluster labels.
+        """Compute clusters from a data or distance matrix and predict labels.
 
         Parameters
         ----------
@@ -405,15 +404,15 @@ def fit_predict(self, X, y=None, sample_weight=None):
             ``metric='precomputed'``. If a sparse matrix is provided, it will
             be converted into a sparse ``csr_matrix``.
 
+        y : Ignored
+            Not used, present here for API consistency by convention.
+
         sample_weight : array-like of shape (n_samples,), default=None
             Weight of each sample, such that a sample with a weight of at least
             ``min_samples`` is by itself a core sample; a sample with a
             negative weight may inhibit its eps-neighbor from being core.
             Note that weights are absolute, and default to 1.
 
-        y : Ignored
-            Not used, present here for API consistency by convention.
-
         Returns
         -------
         labels : ndarray of shape (n_samples,)