I want to build a prediction model so I plan to use LASSO and elastic net (with glmnet). I was intending to split into 2/3 train, find the best shrinkage lambda (via CV in the training data) and predict on the remaining 1/3 (by c-statistic).

I was planning on say 50 test training splits to see if the same predictors were selected repeatedly and to see the variability in best lambda and estimated c-statistics in the test dataset.

Then I was then going to obtain a final model using all the data, using the shrinkage parameter found by cross-validating the entire data set. I'd then claim the c-statistic of that final model is within the range of the c-statistics found from the 50 train/test splits (maybe around the mean). Are there are flaws in this scheme or room for improvement ??