So this is a technique from classical statistics, but basically a common approach is to take a raw set of data and normalize it before applying a fit function. You could run data with an exponential distribution through a log transform, for example. One of the risks if you don't is that the leverage from a few points can be very high and skew your model. There's tradeoffs to this approach and many different techniques that can help you find a good model, but data normalization is often a helpful technique.

I think in general it can be helpful in data preparation as one tool that's available to use. But there's an entire field on this and I don't want to oversimplify.