Giving an overview about true positives(TP), false positives (FP), true negatives(TN) and false negatives(FN), helps to get a first impression of the performance of a classifier. The ground truth based view (left) gives information about e.g. how many of the positive elements were classified as positives and the classification based view (right) informs about how many elements, being classified as positive, are in fact positive. They give a very good impression of TPR, TNR, precision, and NPV (negative predictive value).
The Receiver Operating Characteristic (ROC) curve is created by plotting the true positive rate against the false positive rate. The Area Under Curve (AUC) value can be used for model comparison. The ROC AUC changes, if the order of positive and negative elements within the ranking is changed. The order of multiple positive or negative elements is not considered, since there is no way for the ROC AUC to determine the better of two positive elements.