ROC AUC score is better if test data is imbalanced
ROC normally reports higher scores for imbalanced data sets because it does not take into account false discoveries. For this reason, it is recommended to only use ROC when the positive and negative sets are roughly equal in size. Otherwise, the suggestion is to compute the area under the Precision-Recall Curve instead.

You can read a bit about the reasons here.

https://machinelearningmastery.com/roc-curves-and-precision-recall-curves-for-classification-in-python/


  However, ROC curves can present an overly optimistic view of an algorithm’s performance if there is a large skew in the class distribution. […] Precision-Recall (PR) curves, often used in Information Retrieval , have been cited as an alternative to ROC curves for tasks with a large skew in the class distribution.
  
  — The Relationship Between Precision-Recall and ROC Curves, 2006.