It relates to a very popular word these days:
Think about how poor classifiers might hurt an ensemble that "votes" on the class to predict. They could add noise, sure, but as long as there are enough good classifiers to cast their votes in the right direction, the addition of the poor classifiers is unlikely to do much harm.
Unless, of course, the poor classifiers "team up." There's already too much personification already in this answer, so the point is that highly correlated poor classifiers could overturn the collective answer of the better ones.
Also, positively correlated data leads to less precise estimates in general, because there is less information than your sample size would suggest. For arithmetic means, you can see this in the variance formula for sums of correlated random variables.