The most informative curve for imbalance datasets
Precision-recall curves are argued to be more useful than ROC curves in &quot;The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets&quot; by Saito and Rehmsmeier. They argue that ROC might lead to the wrong visual interpretation of specificity.
F1-score equally balances precision and recall. In some domains it might be more useful to more heavily weight precision (F &lt; 1) or recall (F &gt; 1).