r2 for regression models it is a score or error?
R2 of 0.83 means that approx 83% of the variance can be explained by the model, 17% of the variance can't be explained by the model.

This is one of the measures to evaluate a model, but you shouldn't try and maximize any number, just for the sake of maximizing the number. An high R2 could be a sign of over fitting, and a model with low R2 could still be useful.

But in a simplistic way, yes they could be seen as "score" and "error". But the implementations of the number can vary.