Receiver Operating Characteristic:
The curve between True Positive Rates(TPR) in Y-Axis and False Positive Rates(FPR) in X-Axis is known as the ROC curve. The plot is generated by capturing (TPR, FPR) values for multiple iterations of sampling and predictions.
Area Under the Curve (AUC)
The amount of area covered under the ROC curve. Perfect classification will have its value as 1. A good range for AUC is 0.6-0.9. Which helps to understand the performance of the model. Higher the AUC the better it is. If the value of AUC is less than 0.5 then it means the predictive model is not able to discriminate between the classes.