The idea of model fusions is pretty simple. You combine the predictions of a bunch of separate classifiers into a single, uber-classifier prediction, in theory, better than the predictions of its individual constituents.
As my colleague Teresa Álverez mentioned in a previous post, however, this doesn’t typically lead to big gains in performance. We’re typically talking 5-10% improvements even in the best case. In many cases, OptiML will find something as good or better than any combination you could try by hand.