Which observation to use when doing k-fold validation or boostrap?
I recommend using the second option you presented.  I would use $T$ with 10-fold CV to select my modeling technique and optimal tuning parameters.  Take a look at what performed the best ("best" being the model that gives us the best error, but also doesn't have the error fluctuate too much from fold to fold).  After selecting a model, you can use the model on $V$ to get a realistic error rate.

The reason I don't recommend the first option is: There are varying degrees of over fitting that can occur when going through model selection and model tuning, then using that same data to get an error rate.  CV is a great way to limit this overfitting and it gives us a sense of performance variance which is great, but a classic hold-out validation set is the gold standard for model performance.  In your case the first option might not be wrong (depends a lot on data/techniques), but if a hold-out validation set is available I would go for that.