We present improved methods for using stars found in astronomical exposures to calibrate both star and galaxy colors as well as to adjust the instrument flat field. By developing a spectroscopic model for the SDSS stellar locus in color-color space, synthesizing an expected stellar locus, and simultaneously solving for all unknown zeropoints when fitting to the instrumental locus, we increase the calibration accuracy of stellar locus matching. We also use a new combined technique to estimate improved flat-field models for the Subaru SuprimeCam camera, forming `star flats based on the magnitudes of stars observed in multiple positions or through comparison with available SDSS magnitudes. These techniques yield galaxy magnitudes with reliable color calibration (< 0.01 - 0.02 mag accuracy) that enable us to estimate photometric redshift probability distributions without spectroscopic training samples. We test the accuracy of our photometric redshifts using spectroscopic redshifts z_s for ~5000 galaxies in 27 cluster fields with at least five bands of photometry, as well as galaxies in the COSMOS field, finding sigma((z_p - z_s)/(1 + z_s)) ~ 0.03 for the most probable redshift z_p. We show that the full posterior probability distributions for the redshifts of galaxies with five-band photometry exhibit good agreement with redshifts estimated from thirty-band photometry in the COSMOS field. The growth of shear with increasing distance behind each galaxy cluster shows the expected redshift-distance relation for a flat Lambda-CDM cosmology. Photometric redshifts and calibrated colors are used in subsequent papers to measure the masses of 51 galaxy clusters from their weak gravitational shear. We make our Python code for stellar locus matching available at http://big-macs-calibrate.googlecode.com; the code requires only a catalog and filter functions.
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