What would be a better method for machine learning folks to take? As a sincere curiosity / desire to learn, not meant as a rhetorical implication that I disagree.

I interpret your criticism to mean that ML folks tend to re-use a test set multiple times without worrying that doing so reduces the meaning of the results. If that's what you mean, then I do agree.

Informally, some researchers are aware of this and aim to use a separate validation data set for all parameter tuning, and would like to use a held out test set as few times as possible — ideally just once. But it gets more complicated than that because, for example, different subsets of the data may not really be independent samples from the run-time distribution (example: data points = medical data about patients who lived or died, but only from three hospitals; the model can learn about different success rates per hospital successfully but it would not generalize to other hospitals). In other words, there are a lot of subtle ways in which a held out test set can result in overconfidence, and I always like to learn of better ways to resist that overconfidence.