The correct answer is C. R2 Score. The R2 Score, or coefficient of determination, is the most common default metric for evaluating regression models. It quantifies the proportion of variance in the target variable that the model's predictions explain, with values ranging from 0 to 1. A score of 1 indicates a perfect fit, while 0 suggests no relationship. Other metrics serve different purposes: Mean Absolute Error (MAE) calculates the average absolute difference between predicted and actual values, being less sensitive to outliers than RMSE. Log Loss is tailored for classification problems, assessing the model's accuracy in predicting class probabilities. The F1 Score, another classification metric, balances precision and recall to evaluate the model's effectiveness in identifying positive and negative instances. The R2 Score's popularity stems from its interpretability, bounded range facilitating model comparisons, and its ability to reflect the model's adherence to data trends. While RMSE (Root Mean Squared Error) is also prevalent, its heightened sensitivity to outliers often relegates it to a secondary choice. Selecting the appropriate metric should align with the modeling task's specific objectives, as certain contexts may favor MAE or other metrics over the R2 Score.