Why the seed matters more than the split
An unseeded shuffle gives you a different split every time you run it. That means the evaluation numbers from two runs are not comparable, and a change that appears to improve the model may just be an easier validation set.
The seed here produces a deterministic shuffle: the same input and the same seed always yield the same split. Record the seed alongside your results and the comparison holds.
Choosing the validation share
Ten to twenty percent is the usual range. The consideration is absolute count rather than percentage: a validation set of thirty examples produces noisy metrics regardless of what fraction of the data it represents.
For a small dataset, take a larger share. For a very large one, a few hundred well-chosen examples measure as reliably as several thousand and cost less to run.
When not to shuffle
Shuffle by default, because data assembled chronologically or grouped by source produces a validation set that is systematically different from the training set.
Do not shuffle when order carries meaning that must not leak — most obviously time series, where training on later data and validating on earlier data tests the wrong thing entirely.