Is there any possbility that a ordinary supervised model performs better than a outlier algorithm in this task?

I have tried some outlier detection datasets (ODDs) in this website like Annthyroid dataset (http://odds.cs.stonybrook.edu/annthyroid-dataset/).

However, when I compare some ordinary supervised models (e.g., SVM and Random Forest), the results indicate that SVM and RF are much better than the anomaly detection algorithms like OC-SVM and Isolation Forest.

I was wonder the reason for this weird results, because threoratically the outlier detection algorithms should perform better in the outlier detection task. Could anyone help me figure this problem? Thanks!