AUC ROC metric on a Kaggle competition
As you would have seen in the research, AUC ROC prioritizes getting the order of the predictions correct, rather than approximating the true frequencies.

Usually, like in the credit card fraud problem you link to, the impact of one or two false negative is more devastating that many false positives. If those classes are imbalanced, like they are in the fraud case, AUC ROC is a bad idea.

It appears that in the competition you are referring to, the hosts are more interested in labeling which comments are more toxic than others rather than rating how toxic they each are. This makes sense since in reality the labels are subjective.