Can you reuse observations from your train data in your final test data?
The Out of Bag(OOB) error in XG Boost is considered as a Validation error(test error). During Hyper-parameter selection in XG boost, OOB error is measured for different hyperparameters. In XG Boost algorithm, the decision trees are constructed by a random sample of observations and the successive decision trees are built by giving more importance to the observation which the previous trees are misclassified. As this XG boost keeps increasing the number of decisions trees, the validation error decreases and saturates at some point.

In your case, you can consider this OOB error as test error instead of creating a separate test set.

The following plot explains this more accurately