The leaders in the NetflixPrize competition for the last couple years have utilized ensembles of large numbers of models with a fairly straightforward integration procedure. You can only get so far with a given model, but if you randomly scramble its hyperparameters or training procedure, and then average multiple runs together, you will improve your performance. The logical path forward is to derandomize this procedure, and figure out how to predict, a priori, which model probabilities become more accurate and which don't. But of course until you figure out how to do that, random is better than nothing.

As a process methodology, it seems useful to try random variations, find the ones that outperform and THEN seek to explain it.