Under the frequentist interpretation of probability, 95% of having a "correct assumption" means that as you observe iid experiments of this assumption, the long-run average of them will have 95/100 of such experiments exhibit the assumption as true. But "experiment" here isn't a physics experiment, it's a _universe_ in which this assumption could be true. We're in some _fixed_ universe. This assumption holds in ours with probability 0 or 1 (we don't know which).
So, I think the 95% you're referring to here is a Bayesian belief level. This is coherent in that it doesn't require some ambient set of multiple universes to meaningfully describe what is meant by "probability", but the prior required for this interpretation is a bit of weird beast. That is, we have some prior over the space of all possible assumptions for models of physics and update our beliefs based on evidence (we can collect evidence from our single universe multiple times and update our beliefs in a coherent way). In the limit of evidence, this prior matters less, but constants matter here! How much evidence do we need before we can be confident the "belief levels" we're throwing around aren't that subjective anymore? We don't really have a good sense for what the structure over this space of "assumptions of physical models" is, so we can't really answer this question.
Within specific settings for statistical learning theory, we find that simple models generalize well. But that's an implication: _if_ you have a class of simple models and it fits the data well, _then_ you'll generalize well. When it comes to answering the question, "Which theories generalize well?", such analysis is incomplete.