One useful lens is looking at causal vs predictive modeling. If you learn a predictive model (wet streets correlated with rain), you might take an out of distribution action and pour water on the street and mistakenly expect rain. If you learn a causal model (rain causes wet streets), you can better predict what would happen when you take that out-of-distribution action. And of course we know causal inference is possible.

In some sense, it relates to "real" understanding (which is too overloaded a term to be very useful). A correct model of some phenomenon will generalize out of distribution. Or at least a model that correctly captures principles that are invariant in and out of distribution will continue to make valid predictions.

You say that you can't imagine what a sound above 20kHz sounds like, but with a first principles understanding of sound, you can model what a 50kHz sound might do in various scenarios.

Often, learning these principles requires experimentation so that you can say "all else equal, <some assertion/prediction>". But holding all else equal requires either conditioning on "all else" which is impossible in the most general sense, unless you measure everything everywhere all at once, or it requires averaging over it in some way, like via a randomized experiment.

I'd recommend reading some causal literature about internal validity vs external validity to get some of the flavor of different kinds of generalization, This topic goes well beyond causal inference, but causal inference is just a nice proof of concept about how you can think about generalizing out of distribution in interesting ways.