Interpreting hamming loss for multilabel classification

I have a multi label – multi class classifier that aims to predict the top 3 selling products out of 11 possible for a given day.

Using scikit learn’s OneVSRest with XgBoost as an estimator, the model gets a hamming loss of 0.25.

Im not familiar with HL, I have mainly done binary classification with roc_auc in the past.

Is this an okay score and how can I describe the effectiveness of the model?
does it mean that the model predicts 0,25 * 11 = 2,75 labels wrong on average?