Rescale parameter in data augmentation
As rightly pointed out by you the rescale=1./255 will convert the pixels in range [0,255] to range [0,1].
This process is also called Normalizing the input. Scaling every images to the same range [0,1] will make images contributes more evenly to the total loss.
Without scaling, the high pixel range images will have a large say to determine how to update weights. For example, black/white cat image could be higher pixel range than pure black cat image, but it just doesn't mean black/white cat image is more important for training.
Also the neural network has a higher chance of converging as it make the coefficients in range of [0,1] as opposed to [0,255] so helps the model process input faster.