The ROC curve is a graph that shows how well the estimated model predicts cases (sensitivity) and non-cases (specificity). What we are interested in here is the “area under the curve” (AUC). The AUC ranges between 0.5 and 1.0. The nearer the AUC is to 1, the better the predictive power. On the other hand, a value of 0.5 suggests that we may just flip a coin to decide on whether the outcome is a case or non-case. Here are some commonly used cut-off points when it comes to AUC: 

Area under the curve (AUC)
0.5-0.6	Fail
0.6-0.7	Poor
0.7-0.8	Fair
0.8-0.9	Good
0.9-1.0	Excellent

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Practical example

Let us first go back to the example from the multiple linear regression analysis. The quietly option is included in the beginning of the command to suppress the output. 

quietly logistic earlyret bmi sex ib1.educ if pop_logistic==1

Then we order the ROC curve:

lroc

The AUC value is 0.64, suggesting that our model has poor predictive power.