I standardize (zero-mean/unit-biased-variance) before calculating autocorr(x) and crosscorr(x,y). The results are biased estimates with unity at zero lag for the autocorrelation.
To obtain unbiased estimates divide by N-1 instead of N for the variance and divide by N-abs(k)-1 instead of N for the kth lag. I didn't like the resulting plots, so I use the biased estimate.
To determine significance levels I averaged over M=100 trials for N = 100 dimensional Normal Gaussian time series. The 95th average absolute value was 0.21 and the 100th was 3.1. Therefore I consider correlation values >= 0.21 as significant.
A noise-free nth order polynomial can be determined by n+1 points. Therefore when I imagine a nth order polynomial fit to a smoothed plot of x, or y vs x, I start thinking about nonzero lags <= n+1.