The basics of epistemology are well explored - everyone ends up with some version of a modelling story. But I would say that the question of purpose is a big gap in most people's thinking. It is just sort of assumed that we know why we model, what the goal is, and so this aspect is not normally formalised.
Broadly, the goal of modelling can be divided into a search after truth vs a search after control. One is about knowing reality in some complete and objective fashion (but in a passive, contemplative, just lookin' at it way). While the other is a pragmatic, utiltarian, operational kind of knowing the world where it does not matter if our models are "true", just that they are functional - they allow us to act on the world and get results that matter to us for some reason.
The difference in goals is actually quite important. The utilitarian one can be argued to be the more natural (it is the way all other systems apart from human philosophers probably operate

).
Something else that is often missed is the importance of reductionism to this goal. A good model is the one that involves recording or otherwise handling the least amount of information.
Knowledge is usually viewed as "seeing every tiny detail". So more information is better. But effective modelling goes the other direction. It demands that you shed information (ie: you generalise to find universal rules, or physical laws). And even the other part of modelling, the predictions and measurements, are as reduced as possible. A good model is one that predicts everything you want from one (perhaps a few) very precise inputs.
Which is why we worship F = ma, for instance. A compact description powered by equally compact measurements. (And any alien culture would have to come up with the same formula, or at least a version of the formula that could be translated in the way that one human language mostly maps onto another in terms of its concepts).
But anyway, back to the quote above. It clearly drives a false wedge between scientific method and human purposes. Scientific method has always been grounded in philosophy (specifically epistemology). And purpose does not get discussed enough. It is a live issue. Particularly because there is this counter-intuitive aspect about modelling - that good models shed as much detail as possible and so tend to look "unreal" to those who wrongly expect science to be a passive kind of "all seeing perception".
For example in mind science, there is a constant lament about the hard problem and the failure of science to deliver "what is it like" type explanations. This just fails to get what modelling is actually ultimately about.