What could make a set of the train data more predictive than the whole train data I took a sample of my training data and balanced it and then trained my model. The results obtained are more accurate than using the whole set of train data (balanced or imbalanced). My question is: what could explain this result? (the entire set of data does not contain any noise).

Thanks in advance.
In absence of noise and if the difference of accuracy you observe is significant, the only reason I see is that, by luck, the distribution of the training data subset happens to be closer to the distribution of your validation data than the distribution of the whole training data.
This should not happen with truly representative validation data (i.e. with training and validation data built by random split of a given dataset).