diff --git a/sklearn/cluster/optics_.py b/sklearn/cluster/optics_.py
index 899da518ae796..94ff3935002a6 100755
--- a/sklearn/cluster/optics_.py
+++ b/sklearn/cluster/optics_.py
@@ -52,11 +52,30 @@ def optics(X, min_samples=5, max_eps=np.inf, metric='euclidean',
         shorter run times.
 
     metric : string or callable, optional (default='euclidean')
-        The distance metric to use for neighborhood lookups. Default is
-        "euclidean". Other options include "minkowski", "manhattan",
-        "chebyshev", "haversine", "seuclidean", "hamming", "canberra",
-        and "braycurtis". The "wminkowski" and "mahalanobis" metrics are
-        also valid with an additional argument.
+        metric to use for distance computation. Any metric from scikit-learn
+        or scipy.spatial.distance can be used.
+
+        If metric is a callable function, it is called on each
+        pair of instances (rows) and the resulting value recorded. The callable
+        should take two arrays as input and return one value indicating the
+        distance between them. This works for Scipy's metrics, but is less
+        efficient than passing the metric name as a string.
+
+        Distance matrices are not supported.
+
+        Valid values for metric are:
+
+        - from scikit-learn: ['cityblock', 'cosine', 'euclidean', 'l1', 'l2',
+          'manhattan']
+
+        - from scipy.spatial.distance: ['braycurtis', 'canberra', 'chebyshev',
+          'correlation', 'dice', 'hamming', 'jaccard', 'kulsinski',
+          'mahalanobis', 'minkowski', 'rogerstanimoto', 'russellrao',
+          'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean',
+          'yule']
+
+        See the documentation for scipy.spatial.distance for details on these
+        metrics.
 
     p : integer, optional (default=2)
         Parameter for the Minkowski metric from
@@ -182,11 +201,30 @@ class OPTICS(BaseEstimator, ClusterMixin):
         shorter run times.
 
     metric : string or callable, optional (default='euclidean')
-        The distance metric to use for neighborhood lookups. Default is
-        "euclidean". Other options include "minkowski", "manhattan",
-        "chebyshev", "haversine", "seuclidean", "hamming", "canberra",
-        and "braycurtis". The "wminkowski" and "mahalanobis" metrics are
-        also valid with an additional argument.
+        metric to use for distance computation. Any metric from scikit-learn
+        or scipy.spatial.distance can be used.
+
+        If metric is a callable function, it is called on each
+        pair of instances (rows) and the resulting value recorded. The callable
+        should take two arrays as input and return one value indicating the
+        distance between them. This works for Scipy's metrics, but is less
+        efficient than passing the metric name as a string.
+
+        Distance matrices are not supported.
+
+        Valid values for metric are:
+
+        - from scikit-learn: ['cityblock', 'cosine', 'euclidean', 'l1', 'l2',
+          'manhattan']
+
+        - from scipy.spatial.distance: ['braycurtis', 'canberra', 'chebyshev',
+          'correlation', 'dice', 'hamming', 'jaccard', 'kulsinski',
+          'mahalanobis', 'minkowski', 'rogerstanimoto', 'russellrao',
+          'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean',
+          'yule']
+
+        See the documentation for scipy.spatial.distance for details on these
+        metrics.
 
     p : integer, optional (default=2)
         Parameter for the Minkowski metric from
@@ -419,8 +457,11 @@ def _set_reach_dist(self, point_index, processed, X, nbrs):
             # Everything is already processed. Return to main loop
             return point_index
 
-        dists = pairwise_distances(P, np.take(X, unproc, axis=0),
-                                   self.metric, n_jobs=1).ravel()
+        if self.metric == 'precomputed':
+            dists = X[point_index, unproc]
+        else:
+            dists = pairwise_distances(P, np.take(X, unproc, axis=0),
+                                       self.metric, n_jobs=None).ravel()
 
         rdists = np.maximum(dists, self.core_distances_[point_index])
         new_reach = np.minimum(np.take(self.reachability_, unproc), rdists)
diff --git a/sklearn/cluster/tests/test_optics.py b/sklearn/cluster/tests/test_optics.py
index bddf57ec7b5d1..1215746faa4c3 100755
--- a/sklearn/cluster/tests/test_optics.py
+++ b/sklearn/cluster/tests/test_optics.py
@@ -11,6 +11,7 @@
 from sklearn.cluster.optics_ import _TreeNode, _cluster_tree
 from sklearn.cluster.optics_ import _find_local_maxima
 from sklearn.metrics.cluster import contingency_matrix
+from sklearn.metrics.pairwise import pairwise_distances
 from sklearn.cluster.dbscan_ import DBSCAN
 from sklearn.utils.testing import assert_equal, assert_warns
 from sklearn.utils.testing import assert_array_equal
@@ -436,3 +437,15 @@ def test_reach_dists():
     else:
         # we compare to truncated decimals, so use atol
         assert_allclose(clust.reachability_, np.array(v), atol=1e-5)
+
+
+def test_precomputed_dists():
+    redX = X[::10]
+    dists = pairwise_distances(redX, metric='euclidean')
+    clust1 = OPTICS(min_samples=10, algorithm='brute',
+                    metric='precomputed').fit(dists)
+    clust2 = OPTICS(min_samples=10, algorithm='brute',
+                    metric='euclidean').fit(redX)
+
+    assert_allclose(clust1.reachability_, clust2.reachability_)
+    assert_array_equal(clust1.labels_, clust2.labels_)