Do I need to calculate the AUROC for both my training and validation set? I have come across papers where they have calculated the AUROC for both a training and a testing set;
 
When I am using the package MLeval, I have used my training data set here 
     randomforestfit1 <- train(T2DS ~ ., 
               data = mod_train.newy, 
               method = "rf",     
               trControl = trainControl(method = "repeatedcv", 
                                     number = 10, 
                                     repeats = 5, 
                                     savePredictions= TRUE, 
                                     classProbs= TRUE, 
                                     verboseIter = TRUE))

 ##

  x <- evalm(randomforestfit)

 ## get roc curve plotted in ggplot2

 x$roc

 ## get AUC and other metrics

x$stdres

My AUROC for metabolites+ visceral fat + crp-1 is 0.82
My AUROC for visceral fat and crp-1 is 0.69
When using my validation set it is 0.88 and 0.86 respectively. I thought it was better to mention only the validation set rather than both. Please can anybody advise?
 A: YES, report both.
Comparing performance on training data vs out-of-sample data can give an idea of whether performance could be improved via bias reduction or variance reduction. If both have poor performance, you would suspect that you haven't captured the trends in the data; you need more parameters or perhaps additional variables to explain the outcome. (This is high bias.) If you have strong in-sample performance but weak out-of-sample performance, you would suspect that you have overfit to the training data. (This is high variance.)
You might be thinking that this is important to track in your group but not important to report. I disagree. You publish work so that others can build on your work. For someone to build upon your work, they should see where the weaknesses are. This is why other papers are reporting both.
Forgottenscience is too dismissive of in-sample performance but does allude to the valid point that you should not be happy with performance unless you get adequate performance from out-of-sample testing, not in-sample testing.
A: No one should care about the performance on the training set, the key quantity is whether you can generalize out of sample. Having the area under the ROC curve be 1 for the training data is irrelevant (and occasionally achievable with random forests), and thus you'd in any case need to include the same estimates for the validation set and/or test set to provide useful information on your classifier. 
Again, whatever you report on the training set can be overfit to an arbitrary degree, the only real test of value is hold-out set performance. 
