# Machine Learning: if test, validation errors are same, the model with lower train loss is better at generalization?

I tuned some hyperparameters and got 2 best models.

These two models are almost the same "test(not train)" and "validation" errors. But model A has lower "train" error than model B.

In this case, the model with lower "train" error (model A), would show good performance on new data? (good generalization)

Or model A is more overfitting to the dataset so it would work worse in new data?

Thanks