Error is the difference between true values & predicted values basically

R^2 or Adjusted R^2 is the default score evaluation metric, for estimating accuracy

Difference b/w R^2 & Adjusted R^2

While R^2 tends to increase as more variables are added to the model (even if they don’t improve the model significantly),
Adjusted R^2 reduces accuracy for needless features, avoiding Overfitting.

It considers the number of predictors in the model and adjusts R-squared accordingly