I think a nicer analogy are spectral gaps. Obviously, no reasonable finite model will be both correct and useful, outside of maybe particle physics; so you need to choose some cut-off of you model’s complexity. The cheapest analogy is when you try to learn a linear model, e.g. PCA/SVD/LSA (all the same).
A good model is one that hits a nice spectral gap: Adding a couple of extra epicycles gives only a very moderate extra accuracy. If there are multiple nice spectral gaps, then you should keep in mind a hierarchy of successively more complex and accurate models. If there are no good spectral gaps, then there is no real preferred model (of course model accuracy is only partially ordered in real life). When someone proposes a specific model, you need to ask both “why not simpler? How much power does the model lose by simplification?”, as well as “Why not more complex? Why is any enhancement of the model necessarily very complex?”.
However, what constitutes a good spectral gap is mostly a matter of taste.