1. The general idea of using a validation set is to analyse or control the training of the model. Usually, the model tends to under-fit or over-fit, and this can be observed using the validation split. In practical applications, we just have the visible training set and unknown test set. So, with some assumption on the test set, a simple assumption can be such that test set belongs to the similar distribution as of the training set, we use part of training set as validation set. Once the validation set is chosen, we train the system on the training set and at every required step we observe how the trained model performs on the validation set. (Here, we are still under an assumption that the test-set is not visible). Hence, we can use the validation to check if the model is being trained well and to control the training (early stopping) especially in the case of over-fitting. 964
With this assumption a good validation set should be something which has similar distribution as that of the test set. However, most of the times since we split the available dataset into training, validation and test set, the validation set usually has similar distribution as that of test set.
2. As mentioned above, difference in the distributions of test set and validation set can cause some discrepancies in the results. As you pointed out, if we upsample the training set before obtaining the validation split, then the validation set will contain different distribution than that of the test set. The discrepancies also occur when the the test set is not provided before-hand, and if it has different distribution than that of training/validation set.
3. Generally, a random-uniform-split produces the similar distribution, i.e, the ratio of imbalance reflects in all the splits. However, when the number of samples are small (either overall number of samples or number of samples in minority class), the ratio may not be proportional across the splits. Hence, given an option to have stratified-split, you can prefer the stratified split. (As mentioned, it may not make much difference if random-uniform-distribution produces similar ratios across the split). 964