diff --git a/feature_selection/find_signature.py b/feature_selection/find_signature.py
index c01a1f2111a..4e63f4d2967 100644
--- a/feature_selection/find_signature.py
+++ b/feature_selection/find_signature.py
@@ -19,8 +19,8 @@
 ### remainder go into training)
 ### feature matrices changed to dense representations for compatibility with
 ### classifier functions in versions 0.15.2 and earlier
-from sklearn import cross_validation
-features_train, features_test, labels_train, labels_test = cross_validation.train_test_split(word_data, authors, test_size=0.1, random_state=42)
+from sklearn import model_selection
+features_train, features_test, labels_train, labels_test = model_selection.train_test_split(word_data, authors, test_size=0.1, random_state=42)
 
 from sklearn.feature_extraction.text import TfidfVectorizer
 vectorizer = TfidfVectorizer(sublinear_tf=True, max_df=0.5,
diff --git a/outliers/outlier_removal_regression.py b/outliers/outlier_removal_regression.py
index d509cd9f22f..789eb7c139a 100644
--- a/outliers/outlier_removal_regression.py
+++ b/outliers/outlier_removal_regression.py
@@ -20,7 +20,7 @@
 ### and n_columns is the number of features
 ages       = numpy.reshape( numpy.array(ages), (len(ages), 1))
 net_worths = numpy.reshape( numpy.array(net_worths), (len(net_worths), 1))
-from sklearn.cross_validation import train_test_split
+from sklearn.model_selection import train_test_split
 ages_train, ages_test, net_worths_train, net_worths_test = train_test_split(ages, net_worths, test_size=0.1, random_state=42)
 
 ### fill in a regression here!  Name the regression object reg so that
diff --git a/pca/eigenfaces.py b/pca/eigenfaces.py
index 074b860a253..b9ad1ccb9f2 100644
--- a/pca/eigenfaces.py
+++ b/pca/eigenfaces.py
@@ -23,12 +23,12 @@
 import pylab as pl
 import numpy as np
 
-from sklearn.cross_validation import train_test_split
+from sklearn.model_selection import train_test_split
 from sklearn.datasets import fetch_lfw_people
-from sklearn.grid_search import GridSearchCV
+from sklearn.model_selection import GridSearchCV
 from sklearn.metrics import classification_report
 from sklearn.metrics import confusion_matrix
-from sklearn.decomposition import RandomizedPCA
+from sklearn.decomposition import PCA
 from sklearn.svm import SVC
 
 # Display progress logs on stdout
@@ -70,7 +70,10 @@
 
 print "Extracting the top %d eigenfaces from %d faces" % (n_components, X_train.shape[0])
 t0 = time()
-pca = RandomizedPCA(n_components=n_components, whiten=True).fit(X_train)
+pca = PCA(
+    n_components=n_components,
+    svd_solver='randomized',
+    whiten=True).fit(X_train)
 print "done in %0.3fs" % (time() - t0)
 
 eigenfaces = pca.components_.reshape((n_components, h, w))
diff --git a/tools/startup.py b/tools/startup.py
index 4638e0d115e..c3d2bc521ab 100644
--- a/tools/startup.py
+++ b/tools/startup.py
@@ -32,7 +32,22 @@
 print "download will complete at about 423 MB"
 import urllib
 url = "https://www.cs.cmu.edu/~./enron/enron_mail_20150507.tar.gz"
-urllib.urlretrieve(url, filename="../enron_mail_20150507.tar.gz") 
+filename = "../enron_mail_20150507.tar.gz"
+try: 
+    urllib.urlretrieve(url, filename=filename)
+except IOError as socket_error:
+    expected_error = (
+        "IOError('socket error', SSLError(1, u'[SSL: DH_KEY_TOO_SMALL]"+
+        " dh key too small (_ssl.c:727)'))"
+        )
+    if repr(socket_error) == expected_error:
+        import ssl
+        cipher = "ECDHE-RSA-AES128-GCM-SHA256"
+        context = ssl.create_default_context()
+        context.set_ciphers(cipher)
+        urllib.urlretrieve(url, filename=filename, context=context)
+    else:
+        raise socket_error
 print "download complete!"