Calculation formula：

 - Precision：P=TP/(TP+FP)
 - Recall：R=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 PR (Precision-Recall) curve(F1-score, Precision, Recall) is also the same criteria.

Real data will face the imbalance problem, namely the imbalance between positive and negative samples. The ROC/AUC curve can remain curve, but the PR change  
intensely when the testing set occurs imbalance.

So in the real world, PR curve used more actually since positive and negative samples very uneven. ROC/AUC curve does not reflect the performance of the classifier, but PRC 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 have high interpretative.