# What are the pros and cons of different metrics for evaluating a logistic regression model?

In the data science world, I have always evaluated the performance of logistic regression models simply using ROC/AUC. However recently, I've read from some traditional statistics source about some measures of a logistic regression, including:

for goodness-of-fit: Pearson statistic, Deviance, and Hosmer-Lemeshow statistic; for quality of prediction: Nagelkerke's $R^2$, Concordance measure.

So, which metrics are better for what scenario? Also, I'm pretty confused how goodness-of-fit is different from predictability?