ROC Curve is an important method of model selection, but its uncertainty affects the accuracy of model selection. Based on discernible granularity and the view of reflecting the score's uncertainty, the study proposes the concept of gROC and gAUC, and discusses, theoretically, some properties of the gROC. The study also tests the reasonableness of gROC using binormal model after gave its algorithm. On this basis, the paper also proposes two model selection measures, λAUC and ρAUC. The effieciency of these measures is verified based on UCI data sets. Experimental results show that the gROC can effectively reflect the uncertainty of ROC curve, and the model selection methods based on λAUC and ρAUC are better than the method based on AUC or sAUC. In some cases, gROC has stronger capability on comparison of classifiers performance.