So much depends on scale. I wish I could count on having more than 2,000-3,000 cases like @RyanZotti typically has; I seldom have 1/10th that many. That's a big difference in perspective between "big data" machine learning folk and those working in fields like biomedicine, which might account for some of the different perspectives you will find on this site.
I'll present a heuristic explanation of my take on this problem. The basic issue in overfitting, as described on the Wikipedia page, is the relation between the number of cases and the number of parameters you are evaluating. So start with the rough idea that if you have M models you are choosing among and p parameters per model then you are evaluating something on the order of Mp parameters in total.
If there is danger of overfitting there are two general ways to pull back to a more generalizable model: reduce the number of parameters or penalize them in some way.
With adequately large data sets you might never come close to overfitting. If you have 20,000 cases and 20 different models with 100 parameters per model, then you might not be in trouble even without penalization as you still have 10 cases per effective parameter. Don't try that modeling strategy with only 200 cases.
Model averaging might be thought of as a form of penalization. In the example of the Kaggler cited by @RyanZotti, the number of cases is presumably enormous and each of the "several thousand" models in the final ensemble individually contributes only a small fraction of the final model. Any overfitting specific to a particular contributing model will not have a great influence on the final result, and the extremely large numbers of cases in a Kaggler competition further reduces the danger of overfitting.
So, as with so many issues here, the only reasonable answer is: "It depends." In this case, it depends on the relation between the number of cases and the effective number of parameters examined, together with how much penalization is being applied.