The training set is what the model actually fits. Its rows adjust the parameters until error on those rows comes down. The validation set is where you make choices: which model, which settings, how long to train. You score candidates on it and keep the winner.
The test set gets used once, at the end. It estimates how the chosen model will do on data nobody tuned against. That is a different question from validation: not which option is best, but how good the winner really is.
A common split is 60 percent training, 20 validation, 20 test. Large datasets can spare far less for the last two. Skip the split and every number you report describes rows the model already memorized.
Rewriting in plainer words…
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