Significant drop from validation accuracy to test accuracy
I don't think this is ususual, I experiance it quite frequently with regression problems. Generally I think it means the model is underspecified so instead of learning the actual relationship between X and y it overfits. Generally I find that I have to do a bit of feature engineering, and ensure I'm only using features that are really necesary - too many features or features with spurious relationships to the target can really hurt a RF model in particular in my experiance. Also make sure as part of your cross validation that you're cross validating parameters which directly influence model complexity i.e. tree depth, number of trees etc.