I have about 2,500 records in my dataset and I'm using a 60-20-20 train-validation-test split. I want to generate a lift chart to gauge the efficacy of my model at ranking cases. To date, I've only been plotting lift curves for training and testing (never-before-seen, the latter) datasets. Should I include in the training line on the chart my validation set, as well, since the model technically "learned from" (i.e. learned the best model hyperparameters from) that dataset as well?
If you want to include the testing results on a data set whose test results were used for optimization (your "validation" set), I'd recommend to do that in a third, separate lift curve (same for any other figure of merit or diagram).
Yes, optimization is part of the model training and the model did learn from these results. We expect the optimization lift curve to be somewhere in between the test results for training (in the narrower sense) and truly independent test sets.
Where the results on the validation (optimization) set are in relation to training and test set results can help you to find whether the optimization didn't go well and/or whether there's trouble with the training in the narrower sense.