I would think of it as fitting a function which is over-parameterised. So there is intrinsically a model of how things are thought to behave, it’s just a relati

I would think of it as fitting a function which is over-parameterised. So there is intrinsically a model of how things are thought to behave, it’s just a relatively simple one that is over-parameterised so it captures patterns in the data. Get it wrong and it’s over-fit, get it wrong another way and it doesn’t generalise, etc. There can also be an internal representation which is not easily interpretable, an internal representation which is usually of a dimensionality much lower than the number of parameters. It is in this sense that it is a brute-force approach because we should like to know the underlying model, but instead we can throw a hugely over-parameterised but relatively simple model at a problem and it will learn (statistically) the underlying phenomenon. Like you say it does much better than a combinatorial brute-force approach.