Is there value in using a model that fails at validation against measurements?
The late George Box famously said:


  All models are wrong. Some models are useful


All models are wrong.

That's important.

All models are wrong.

In any real-world system that we're interested in, the only accurate model of the system, is the system itself. Anything else, any simplification, gives wrong answers.

Some models are useful.

That's important, too.

Some models help us structure our discussions of the uncertainties (H/T Prof Neil Strachan)

Some models allow us to test "what if" scenarios, and look at what the relative changes might be. Now, in the real world, exogenous influences that the model can't account for, would mean that the actual outcomes would be different: but if the model gives a near-enough account of the scale of relative changes, we can still make informed judgements on the basis of the models. Even when it involves pesky things such as turbulence.

Some models give us insights into emergent properties of systems: a lot of the really interesting problems are emergent properties that aren't necessarily obvious from just looking at the basic rules of the system, and any insights we can gain are valuable.

Some models allow us to do experiments on systems that we couldn't possibly experiment on. And although we know the models are wrong, they can at least give us an indication of the range of possible outcomes.

That's not to defend the use of bad models to do bad work. There's far too much of that in my field, and I do not like it. As Simon W says, it is possible to test models to see if they are suitable for a particular purpose. To continue using a model for a purpose where it is known to be inapplicable, is charlatanry, not academia.