Precision and recall > .90 on holdout data

I'm running ML models (XGBoost and elastic net logistic regression) predicting a 0/1 outcome in a post period based on pre period observations in a large unbalanced dataset. I've undersampled from the majority category class to achieve a balanced dataset that fits into memory and doesn't take hours to run.

I understand sampling can distort precision or recall metrics. However I'm testing model performance on a raw holdout dataset (no sampling or rebalancing).

Are my crazy high precision and recall numbers valid?

Of course there could be something fishy with my data, such as an outcome variable measuring post period information sneaking into my variable list. I think I've ruled that out.