The elbow method for K selection visualizes multiple clustering models with different values for K. Model selection is based on whether or not there is an "elbow" in the curve; e.g. if the curve looks like an arm, if there is a clear change in angle from one part of the curve to another.
# Make 8 blobs dataset
X, y = make_blobs(centers=8)# Instantiate the clustering model and visualizer
visualizer = KElbowVisualizer(MiniBatchKMeans(), k=(4,12))
visualizer.fit(X) # Fit the training data to the visualizer
visualizer.poof() # Draw/show/poof the data
yellowbrick.cluster.elbow