Which answer below would not be a key reason to evaluate a Receiver Operating Characteristic $($ROC$)$ Curve?

Quantitative Measure: ROC curves provide a quantitative measure of the diagnostic accuracy of a test by summarizing its performance across all possible thresholds.

Comparison: ROC curves allow for a direct comparison of different tests or models based on their area under the curve $($AUC$),$ where a higher AUC indicates better overall performance.

Threshold Selection: ROC curves help in selecting the optimal threshold for classification that balances sensitivity and specificity based on the specific requirements of the application.

all of the above are key reasons to evaluate ROC curves.