diff --git a/verde/distances.py b/verde/distances.py
index 7a49ddb76..dd0e8afdf 100644
--- a/verde/distances.py
+++ b/verde/distances.py
@@ -15,7 +15,7 @@ def median_distance(coordinates, k_nearest=1, projection=None):
     distance to its *k_nearest* neighbors among the other points in the
     dataset. Sparse uniformly spaced datasets can use *k_nearest* of 1.
     Datasets with points clustered into tight groups (e.g., densely sampled
-    along a flight line or ship treck) will have very small distances to the
+    along a flight line or ship track) will have very small distances to the
     closest neighbors, which is not representative of the actual median spacing
     because it doesn't take the spacing between lines into account. In these
     cases, a median of the 10 or 20 nearest neighbors might be more
diff --git a/verde/model_selection.py b/verde/model_selection.py
index c230e5991..dfff35532 100644
--- a/verde/model_selection.py
+++ b/verde/model_selection.py
@@ -86,7 +86,7 @@ class BlockShuffleSplit(BaseBlockCrossValidator):
         The number of splits generated per iteration to try to balance the
         amount of data in each set so that *test_size* and *train_size* are
         respected. If 1, then no extra splits are generated (essentially
-        disabling the balacing). Must be >= 1.
+        disabling the balancing). Must be >= 1.
 
     See also
     --------