Calculation formula：
- Precision TP/(TP+FP)
- Recall: TP/(TP+FN)
- F1-score： 2/(1/P+1/R)
- ROC/AUC： TPR=TP/(TP+FN), FPR=FP/(FP+TN)
ROC / AUC is the same criteria and the PR (Precision-Recall) curve (F1-score, Precision, Recall) is also the same criteria.
Real data will tend to have an imbalance between positive and negative samples. This imbalance has large effect on PR but not ROC/AUC.
So in the real world, the PR curve is used more since positive and negative samples are very uneven. The ROC/AUC curve does not reflect the performance of the classifier, but the PR curve can.
If you just do the experiment in research papers, you can use the ROC, the experimental results will be more beautiful. On another hand, PR curve use in the real problem, and it has better interpretability.