A Receiver Operating Characteristic (ROC) curve is a graph that shows the performance of a classification model at different classification thresholds. The Area Under the ROC Curve measures the entire two-dimensional area underneath the entire ROC curve, aggregating the measure of the performance across all classification thresholds. 

The AUC value denotes the ability of the model to predict a higher score for positive examples compared to negative examples. The AUC value is a decimal value from 0 to 1. The higher the value of the AUC, the better the ML model is. We use the AUC to measure the quality of the binary classification model. For our recipe, since I used the same dataset for training and testing, the AUC value was very close to 1.