why this naming convention for padding as "Same" and "Valid" in keras
Using valid will essentially use as much of your input as possible, such that the dimensions continue to work. This means there is a chance some input will be trimmed (removed).

same on the other hand, will add padding to allow e.g. the number of filters/convolutions you specify to be applied.

The reason not to simply have padding=True as an option, is that there are more than two options (see snippet from the docs below). Also, that would not be very explicit: using same or valid makes it crystal clear, what the result should be using the given approach.

This is what the documentation says:


  
  padding: One of "valid", "causal" or "same" (case-insensitive).  "valid" means "no padding".  "same" results in padding the input such that the output has the same length as the original input
  


Take a look at this nice answer with a simple example.