They each measure the same thing, more or less, but it's easier to work analytically with squares than absolute values. Similarly, we tend to work with the variance rather than with expected absolute deviations, we calculate sums of squares rather than sums of absolute values, etc.
More fundamentally, root-mean-square is the norm induced by the expectation inner product in the space of random variables. Norms generalize the geometric notion of length, so intuitively RMS is an appropriate measure of the "stochastic distance" from the origin of a random walk after a set number of steps. RMS can likewise be used as an analogue for geometric length for other purposes in a stochastic context, e.g., in calculating the similarity dimension of fractal stochastic processes like Brownian motion.
More fundamentally, root-mean-square is the norm induced by the expectation inner product in the space of random variables. Norms generalize the geometric notion of length, so intuitively RMS is an appropriate measure of the "stochastic distance" from the origin of a random walk after a set number of steps. RMS can likewise be used as an analogue for geometric length for other purposes in a stochastic context, e.g., in calculating the similarity dimension of fractal stochastic processes like Brownian motion.