The RGB color format can represent any standard color or brightness using a combination of Red, Green and Blue components. For efficiency, this is typically stored as a 24-bit number using 8-bits for each color component (0 to 255) so that for example, White is made of 255 Red + 255 Green + 255 Blue. This is pretty much the same technique that nearly all computer screens have used for decades, and so it is the standard color format used in computer software. Unfortunately when it comes to computer vision, RGB values will vary a lot depending on strong or dim lighting conditions and shadows, etc. In comparison, HSV is much better at handling lighting differences, and it gives you an easy to use color value.
HSV means Hue-Saturation-Value, where the Hue is the color. And since color is not an easy thing to separate or compare, Hue is often represented as a circular angle (between 0.0 to 1.0 when stored as floats). Being a circular value means that 1.0 is the same as 0.0. For example, a Hue of 0.0 is red, a Hue of 0.25 would be green, a Hue of 0.5 is blue, a Hue of 0.75 is pink, and a Hue of 1.0 would be the same as a Hue of 0.0 which is red (again). Saturation is the greyness, so that a Saturation value near 0 means it is dull or grey looking whereas as a Saturation value of 0.8 might be a very strong color (eg: red if Hue is 0). And Value is the brightness of the pixel, so 0.1 is black and 0.9 is white. Unfortunately, there are different ways to represent HSV colors, such as whether a full brightness V of 1.0 should be bright white or a bright color. Most software chooses full brightness V to mean White, whereas OpenCV chooses full brightness V to mean a bright color!
(For a lot more info about HSV and other color spaces, go to HSL and HSV on Wikipedia).