I think you are misunderstanding the concept of an ROC curve. I also had the same problem when I first learned about it.
You can draw an 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 model that returns probabilities. 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, resulting in having a sensitivity and a specificity.
Now on the same model I can 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. Compute 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 an ROC curve from the scratch, but there are packages, what language are you using?