Our current data is incomplete. It's possible then that there's more than one model that fits it, and that our current model is wrong but still manages to fit the data that we have. If we train a naive learning system on the same data, it might find another model that also fits the data but is closer to the right model.

As a historical precedent consider Newtonian vs. Einsteinian mechanics. Newtonian mechanics works fine at familiar scales of time, space, force, power, mass, etc. Einsteinian mechanics also works fine at those scales. If that's all the data you have, they are equivalent theories. Yet Einsteinian mechanics also predicts things far beyond that scale correctly while Newtonian mechanics does not.