There is a lovely web site that explains this in detail. http://www.anaesthetist.com/mnm/stats/roc/
A ROC plot is obtained by plotting all sensitivity values
(true positive fraction) on the y
axis against their equivalent (1 - specificity) values (false positive
fraction) for all available thresholds on the x axis, as in the example shown
below. The example concerns predicting locations that contain an eagle nest.
The area under the ROC function (AUC) is usually taken to be an important index because it provides a single measure of overall accuracy