Histogram presents a pictorial representation of the frequency distribution (and hence an approximation to the probability density function) of a continuous, univariate random variable.

From the perspective of data visualization, Box plots plays also an important role which summarizes information on the median, the 1st and 3rd Quartile for a given sample from a population.

A histogram is a visual representation of a continuous data distribution. It consists of bar that depicts the information of frequency of data points. Each bar is constructed by dividing the entire data into equal intervals.

Histogram tell us the shape of data distribution. It give us an idea of

1. mean,

2. skewness (right or left tailed)

3. heavy tailed or not

The replies up to now to your question "What is [not 'are'] the importance of histograms in visualizing data distributions" mainly state what a histogram is, less so why it is important.

I would not recommend searching for an answer using wide internet search (via Startpage or Google) -- a good starting point is the Wikipedia article "Histogram" (see https://en.wikipedia.org/wiki/Histogram) where you can find further references or a good introductory statistics textbook.

Note that one problem of histograms to visualize the distribution of the data (perhaps to see whether the distribution is normal or skewed -- one can also overlay a histogram with density, not the frequency, on the y-axis with curves of theoretical distributions such as a normal distribution) is that its shape is strongly determined by the choice of the number of bins (or the widths of the intervals) and (if the sample is comparatively small) whether the right or left limit of the interval defining the bins is including or excluding the left or right value. An alternative that does not suffer from this problem (although it has others) is the use of so called kernel density plots, see https://modelinginbiology.github.io/looking-at-the-distribution where you can experiment with different numbers of bins to see how the bin number (or interval width) affects the shape of a histogram.

Another alternative to histograms are quantile-quantile plots (see https://en.wikipedia.org/wiki/Q%E2%80%93Q_plot) that directly compare the distribution of your data against another probability distribution -- it takes some time learning how to read it, but it has the advantage of showing you the shape as compared to another distribution and each data point (at least at the extremes).

Histograms are used see whether the distribution of the data meets a certain criterion (e.g. whether data are normally or Poisson distributed if your analysis or model requires normally or Poisson distributed data) and may therefore be important if you have to decide which statistical analysis or model you should use. They are not suited to identify outliers or extreme values -- quantile-quantile plots or box plots (see https://en.wikipedia.org/wiki/Box_plot) are better suited for the latter.