Understanding the Gini/AUC metric as out-of-development performance metric
The advantage which train/test/validation dataset separation has is that you separate your dataset into:


The individuals which you know the exogenous variables and the output: Training
The individuals which you know the exogenous variables and the output (but you suppose you don't know which the output is): Test
The individuals you know the exogenous variables but not the output: Validation


Every DS or ML model is made so it is prepared to receive a validation dataset in the future and try to get every metric just almost as good as if it was the train dataset.

The test dataset has the objective of simulating the situation of having data but not output, and then you have the output to measure the behaviour and comparing the modelled vs real output.

So, for a concrete answer:
The behaviour you should expect from the validation (or newly collected sample) is the same as the test dataset.

Given that the underlying phenomenon and sampling technique remains the same.

For more information:
https://towardsdatascience.com/train-validation-and-test-sets-72cb40cba9e7