For those downvoting this comment -- it's absolutely true that model performance is discussed at length early in ML courses (usually in the context of the bias-variance tradeoff).

My only quibble would be that precision + recall are one set of evaluation metrics applicable to classification tasks. Modelers can absolutely use other loss functions.

Additionally, precision/recall do not map nicely to regression problems, so people use other metrics (RMSE, MAE, etc.).