In general, the ROC is used for many different levels of thresholds and thus it has many F score values. F1 score is applicable for any particular point on the ROC curve.
You may think of it as a measure of precision and recall at a particular threshold value whereas AUC is the area under the ROC curve. For F score to be high, both precision and recall should be high.
When you have a data imbalance between positive and negative samples, you should always use F1-score because of ROC averages over all possible thresholds.
