When applied to the models themselves it's not a particularly helpful analogy. Machine learning models are largely curve fitting in high dimensional spaces. Overfitting, overspecialization, and the like are problems, and you could relate them to ecological notions and selection, but it's not terribly helpful in practice.

Where it does apply is to the cascade of dependencies among data sets, code that generates them, and sources of signal that you see in large data platforms.