I think you are misunderstanding the concept of a ROC curve. I also had the same problem when I first was exposed to the idea. You can draw a ROC curve on a SINGLE model. You need to change the threshold of your classification for the same model. For instance, imagine I have a logistic regression. I will have my function return probabilities instead of labels. Now if I assign P<0.5 ( probability greater than 0.5) to be class 1 and P<0.5 to be class 0. That will change my True positives and negatives and False positives and negatives, making me have a sensitivity and specificity. Now on the same model I will change the threshold, from say 0.1 to 0.9, such that for example, P>0.9 means class 1 and P<0.9 is class 0. Gather the sensitivity and specificity for all these thresholds and plot them on a sensitivity vs 1-specificity, and you should have your ROC curve. They should both go from 0 to 1. It is fairly simple to write a ROC curve from the scratch, but there are packages, what language are you using?