Synthetic data validation is the step where you confirm that generated data is actually good enough to use in place of the real thing. Generating realistic-looking records is the easy part.
The hard question is whether the synthetic set still carries the structure, the statistical distributions, and the predictive signal that a downstream model depends on. Validation answers that by comparing the synthetic data against the original on those properties and by checking how a model trained on it performs.
Without that check, synthetic data that looks convincing can quietly degrade a model, which is why validation, not generation, is what makes synthetic data trustworthy for training.