Mixture models are currently a bit different. They are basically clusterers, except they are probabilistic, and are applied to inductive problems unlike many clusterers. But they are unlike clusterers in API:

• they have an n_components parameter, with identical purpose to n_clusters
• they do not store the labels_ of the training data
• they do not have a fit_predict method

And they are almost entirely documented separately.

Should we make the MMs more like clusterers?