The coefficient of determination (R²) is the most appropriate metric for determining how closely data fits a regression line in linear regression. R² measures the proportion of variance in the dependent variable that is predictable from the independent variable(s), directly indicating how well the regression line fits the data. While RMSE and MAE measure prediction error magnitude, they don't directly quantify the 'fit' to the regression line like R² does. Recall and precision are classification metrics, not regression metrics. The community discussion strongly supports B (100% consensus), with multiple comments explaining that R² specifically measures how well data fits the regression line, while MAE/RMSE only indicate error magnitude without context about the fit quality.